{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Timeseries\n",
    "\n",
    "Pandas started out in the financial world, so naturally it has strong timeseries support.\n",
    "\n",
    "The first half of this post will look at pandas' capabilities for manipulating time series data.\n",
    "The second half will discuss modelling time series data with statsmodels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pandas_datareader.data as web\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set(style='ticks', context='talk')\n",
    "\n",
    "if int(os.environ.get(\"MODERN_PANDAS_EPUB\", 0)):\n",
    "    import prep # noqa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's grab some stock data for Goldman Sachs using the [`pandas-datareader`](http://pandas-datareader.readthedocs.io/en/latest/) package, which spun off of pandas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-01-03</th>\n",
       "      <td>126.70</td>\n",
       "      <td>129.44</td>\n",
       "      <td>124.23</td>\n",
       "      <td>128.87</td>\n",
       "      <td>112.34</td>\n",
       "      <td>6188700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-04</th>\n",
       "      <td>127.35</td>\n",
       "      <td>128.91</td>\n",
       "      <td>126.38</td>\n",
       "      <td>127.09</td>\n",
       "      <td>110.79</td>\n",
       "      <td>4861600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-05</th>\n",
       "      <td>126.00</td>\n",
       "      <td>127.32</td>\n",
       "      <td>125.61</td>\n",
       "      <td>127.04</td>\n",
       "      <td>110.74</td>\n",
       "      <td>3717400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-06</th>\n",
       "      <td>127.29</td>\n",
       "      <td>129.25</td>\n",
       "      <td>127.29</td>\n",
       "      <td>128.84</td>\n",
       "      <td>112.31</td>\n",
       "      <td>4319600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-09</th>\n",
       "      <td>128.50</td>\n",
       "      <td>130.62</td>\n",
       "      <td>128.00</td>\n",
       "      <td>130.39</td>\n",
       "      <td>113.66</td>\n",
       "      <td>4723500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close  Adj Close   Volume\n",
       "Date                                                          \n",
       "2006-01-03  126.70  129.44  124.23  128.87     112.34  6188700\n",
       "2006-01-04  127.35  128.91  126.38  127.09     110.79  4861600\n",
       "2006-01-05  126.00  127.32  125.61  127.04     110.74  3717400\n",
       "2006-01-06  127.29  129.25  127.29  128.84     112.31  4319600\n",
       "2006-01-09  128.50  130.62  128.00  130.39     113.66  4723500"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs = web.DataReader(\"GS\", data_source='yahoo', start='2006-01-01',\n",
    "                    end='2010-01-01')\n",
    "gs.head().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There isn't a special data-container just for time series in pandas, they're just `Series` or `DataFrame`s with a `DatetimeIndex`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Special Slicing\n",
    "\n",
    "Looking at the elements of `gs.index`, we see that `DatetimeIndex`es are made up of `pandas.Timestamp`s:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the elements of `gs.index`, we see that `DatetimeIndex`es are made up of `pandas.Timestamp`s:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2006-01-03 00:00:00')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.index[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A `Timestamp` is mostly compatible with the `datetime.datetime` class, but much amenable to storage in arrays.\n",
    "\n",
    "Working with `Timestamp`s can be awkward, so Series and DataFrames with `DatetimeIndexes` have some special slicing rules.\n",
    "The first special case is *partial-string indexing*. Say we wanted to select all the days in 2006. Even with `Timestamp`'s convenient constructors, it's a pai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-01-03</th>\n",
       "      <td>126.699997</td>\n",
       "      <td>129.440002</td>\n",
       "      <td>124.230003</td>\n",
       "      <td>128.869995</td>\n",
       "      <td>112.337547</td>\n",
       "      <td>6188700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-04</th>\n",
       "      <td>127.349998</td>\n",
       "      <td>128.910004</td>\n",
       "      <td>126.379997</td>\n",
       "      <td>127.089996</td>\n",
       "      <td>110.785889</td>\n",
       "      <td>4861600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-05</th>\n",
       "      <td>126.000000</td>\n",
       "      <td>127.320000</td>\n",
       "      <td>125.610001</td>\n",
       "      <td>127.040001</td>\n",
       "      <td>110.742340</td>\n",
       "      <td>3717400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-06</th>\n",
       "      <td>127.290001</td>\n",
       "      <td>129.250000</td>\n",
       "      <td>127.290001</td>\n",
       "      <td>128.839996</td>\n",
       "      <td>112.311401</td>\n",
       "      <td>4319600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-09</th>\n",
       "      <td>128.500000</td>\n",
       "      <td>130.619995</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>130.389999</td>\n",
       "      <td>113.662605</td>\n",
       "      <td>4723500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2006-01-03  126.699997  129.440002  124.230003  128.869995  112.337547   \n",
       "2006-01-04  127.349998  128.910004  126.379997  127.089996  110.785889   \n",
       "2006-01-05  126.000000  127.320000  125.610001  127.040001  110.742340   \n",
       "2006-01-06  127.290001  129.250000  127.290001  128.839996  112.311401   \n",
       "2006-01-09  128.500000  130.619995  128.000000  130.389999  113.662605   \n",
       "\n",
       "             Volume  \n",
       "Date                 \n",
       "2006-01-03  6188700  \n",
       "2006-01-04  4861600  \n",
       "2006-01-05  3717400  \n",
       "2006-01-06  4319600  \n",
       "2006-01-09  4723500  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.loc[pd.Timestamp('2006-01-01'):pd.Timestamp('2006-12-31')].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thanks to partial-string indexing, it's as simple as"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-01-03</th>\n",
       "      <td>126.699997</td>\n",
       "      <td>129.440002</td>\n",
       "      <td>124.230003</td>\n",
       "      <td>128.869995</td>\n",
       "      <td>112.337547</td>\n",
       "      <td>6188700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-04</th>\n",
       "      <td>127.349998</td>\n",
       "      <td>128.910004</td>\n",
       "      <td>126.379997</td>\n",
       "      <td>127.089996</td>\n",
       "      <td>110.785889</td>\n",
       "      <td>4861600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-05</th>\n",
       "      <td>126.000000</td>\n",
       "      <td>127.320000</td>\n",
       "      <td>125.610001</td>\n",
       "      <td>127.040001</td>\n",
       "      <td>110.742340</td>\n",
       "      <td>3717400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-06</th>\n",
       "      <td>127.290001</td>\n",
       "      <td>129.250000</td>\n",
       "      <td>127.290001</td>\n",
       "      <td>128.839996</td>\n",
       "      <td>112.311401</td>\n",
       "      <td>4319600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-09</th>\n",
       "      <td>128.500000</td>\n",
       "      <td>130.619995</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>130.389999</td>\n",
       "      <td>113.662605</td>\n",
       "      <td>4723500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2006-01-03  126.699997  129.440002  124.230003  128.869995  112.337547   \n",
       "2006-01-04  127.349998  128.910004  126.379997  127.089996  110.785889   \n",
       "2006-01-05  126.000000  127.320000  125.610001  127.040001  110.742340   \n",
       "2006-01-06  127.290001  129.250000  127.290001  128.839996  112.311401   \n",
       "2006-01-09  128.500000  130.619995  128.000000  130.389999  113.662605   \n",
       "\n",
       "             Volume  \n",
       "Date                 \n",
       "2006-01-03  6188700  \n",
       "2006-01-04  4861600  \n",
       "2006-01-05  3717400  \n",
       "2006-01-06  4319600  \n",
       "2006-01-09  4723500  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.loc['2006'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since label slicing is inclusive, this slice selects any observation where the year is 2006."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second \"convenience\" is `__getitem__` (square-bracket) fall-back indexing. I'm only going to mention it here, with the caveat that you should never use it.\n",
    "DataFrame `__getitem__` typically looks in the column: `gs['2006']` would search `gs.columns` for `'2006'`, not find it, and raise a `KeyError`. But DataFrames with a `DatetimeIndex` catch that `KeyError` and try to slice the index.\n",
    "If it succeeds in slicing the index, the result like `gs.loc['2006']` is returned.\n",
    "If it fails, the `KeyError` is re-raised.\n",
    "This is confusing because in pretty much every other case `DataFrame.__getitem__` works on columns, and it's fragile because if you happened to have a column `'2006'` you *would* get just that column, and no fall-back indexing would occur. Just use `gs.loc['2006']` when slicing DataFrame indexes.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Special Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Resampling is similar to a `groupby`: you split the time series into groups (5-day buckets below), apply a function to each group (`mean`), and combine the result (one row per group)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-01-03</th>\n",
       "      <td>126.834999</td>\n",
       "      <td>128.730002</td>\n",
       "      <td>125.877501</td>\n",
       "      <td>127.959997</td>\n",
       "      <td>111.544294</td>\n",
       "      <td>4.771825e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-08</th>\n",
       "      <td>130.349998</td>\n",
       "      <td>132.645000</td>\n",
       "      <td>130.205002</td>\n",
       "      <td>131.660000</td>\n",
       "      <td>114.769649</td>\n",
       "      <td>4.664300e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-13</th>\n",
       "      <td>131.510002</td>\n",
       "      <td>133.395005</td>\n",
       "      <td>131.244995</td>\n",
       "      <td>132.924995</td>\n",
       "      <td>115.872357</td>\n",
       "      <td>3.258250e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-18</th>\n",
       "      <td>132.210002</td>\n",
       "      <td>133.853333</td>\n",
       "      <td>131.656667</td>\n",
       "      <td>132.543335</td>\n",
       "      <td>115.611125</td>\n",
       "      <td>4.997767e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-23</th>\n",
       "      <td>133.771997</td>\n",
       "      <td>136.083997</td>\n",
       "      <td>133.310001</td>\n",
       "      <td>135.153998</td>\n",
       "      <td>118.035918</td>\n",
       "      <td>3.968500e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2006-01-03  126.834999  128.730002  125.877501  127.959997  111.544294   \n",
       "2006-01-08  130.349998  132.645000  130.205002  131.660000  114.769649   \n",
       "2006-01-13  131.510002  133.395005  131.244995  132.924995  115.872357   \n",
       "2006-01-18  132.210002  133.853333  131.656667  132.543335  115.611125   \n",
       "2006-01-23  133.771997  136.083997  133.310001  135.153998  118.035918   \n",
       "\n",
       "                  Volume  \n",
       "Date                      \n",
       "2006-01-03  4.771825e+06  \n",
       "2006-01-08  4.664300e+06  \n",
       "2006-01-13  3.258250e+06  \n",
       "2006-01-18  4.997767e+06  \n",
       "2006-01-23  3.968500e+06  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.resample(\"5d\").mean().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Open</th>\n",
       "      <th colspan=\"2\" halign=\"left\">High</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Low</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Close</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Adj Close</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-01-08</th>\n",
       "      <td>126.834999</td>\n",
       "      <td>507.339996</td>\n",
       "      <td>128.730002</td>\n",
       "      <td>514.920006</td>\n",
       "      <td>125.877501</td>\n",
       "      <td>503.510002</td>\n",
       "      <td>127.959997</td>\n",
       "      <td>511.839988</td>\n",
       "      <td>111.544294</td>\n",
       "      <td>446.177177</td>\n",
       "      <td>4771825.0</td>\n",
       "      <td>19087300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-15</th>\n",
       "      <td>130.684000</td>\n",
       "      <td>653.419998</td>\n",
       "      <td>132.848001</td>\n",
       "      <td>664.240006</td>\n",
       "      <td>130.544000</td>\n",
       "      <td>652.720001</td>\n",
       "      <td>131.979999</td>\n",
       "      <td>659.899994</td>\n",
       "      <td>115.048592</td>\n",
       "      <td>575.242958</td>\n",
       "      <td>4310420.0</td>\n",
       "      <td>21552100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-22</th>\n",
       "      <td>131.907501</td>\n",
       "      <td>527.630005</td>\n",
       "      <td>133.672501</td>\n",
       "      <td>534.690003</td>\n",
       "      <td>131.389999</td>\n",
       "      <td>525.559998</td>\n",
       "      <td>132.555000</td>\n",
       "      <td>530.220000</td>\n",
       "      <td>115.603432</td>\n",
       "      <td>462.413728</td>\n",
       "      <td>4653725.0</td>\n",
       "      <td>18614900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-29</th>\n",
       "      <td>133.771997</td>\n",
       "      <td>668.859986</td>\n",
       "      <td>136.083997</td>\n",
       "      <td>680.419983</td>\n",
       "      <td>133.310001</td>\n",
       "      <td>666.550003</td>\n",
       "      <td>135.153998</td>\n",
       "      <td>675.769989</td>\n",
       "      <td>118.035918</td>\n",
       "      <td>590.179588</td>\n",
       "      <td>3968500.0</td>\n",
       "      <td>19842500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-02-05</th>\n",
       "      <td>140.900000</td>\n",
       "      <td>704.500000</td>\n",
       "      <td>142.467999</td>\n",
       "      <td>712.339996</td>\n",
       "      <td>139.937998</td>\n",
       "      <td>699.689988</td>\n",
       "      <td>141.618002</td>\n",
       "      <td>708.090011</td>\n",
       "      <td>123.681204</td>\n",
       "      <td>618.406020</td>\n",
       "      <td>3920120.0</td>\n",
       "      <td>19600600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open                    High                     Low  \\\n",
       "                  mean         sum        mean         sum        mean   \n",
       "Date                                                                     \n",
       "2006-01-08  126.834999  507.339996  128.730002  514.920006  125.877501   \n",
       "2006-01-15  130.684000  653.419998  132.848001  664.240006  130.544000   \n",
       "2006-01-22  131.907501  527.630005  133.672501  534.690003  131.389999   \n",
       "2006-01-29  133.771997  668.859986  136.083997  680.419983  133.310001   \n",
       "2006-02-05  140.900000  704.500000  142.467999  712.339996  139.937998   \n",
       "\n",
       "                             Close               Adj Close              \\\n",
       "                   sum        mean         sum        mean         sum   \n",
       "Date                                                                     \n",
       "2006-01-08  503.510002  127.959997  511.839988  111.544294  446.177177   \n",
       "2006-01-15  652.720001  131.979999  659.899994  115.048592  575.242958   \n",
       "2006-01-22  525.559998  132.555000  530.220000  115.603432  462.413728   \n",
       "2006-01-29  666.550003  135.153998  675.769989  118.035918  590.179588   \n",
       "2006-02-05  699.689988  141.618002  708.090011  123.681204  618.406020   \n",
       "\n",
       "               Volume            \n",
       "                 mean       sum  \n",
       "Date                             \n",
       "2006-01-08  4771825.0  19087300  \n",
       "2006-01-15  4310420.0  21552100  \n",
       "2006-01-22  4653725.0  18614900  \n",
       "2006-01-29  3968500.0  19842500  \n",
       "2006-02-05  3920120.0  19600600  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.resample(\"W\").agg(['mean', 'sum']).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can up-sample to convert to a higher frequency.\n",
    "The new points are filled with NaNs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-01-03 00:00:00</th>\n",
       "      <td>126.699997</td>\n",
       "      <td>129.440002</td>\n",
       "      <td>124.230003</td>\n",
       "      <td>128.869995</td>\n",
       "      <td>112.337547</td>\n",
       "      <td>6188700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-03 06:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-03 12:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-03 18:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-04 00:00:00</th>\n",
       "      <td>127.349998</td>\n",
       "      <td>128.910004</td>\n",
       "      <td>126.379997</td>\n",
       "      <td>127.089996</td>\n",
       "      <td>110.785889</td>\n",
       "      <td>4861600.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Open        High         Low       Close  \\\n",
       "Date                                                                  \n",
       "2006-01-03 00:00:00  126.699997  129.440002  124.230003  128.869995   \n",
       "2006-01-03 06:00:00         NaN         NaN         NaN         NaN   \n",
       "2006-01-03 12:00:00         NaN         NaN         NaN         NaN   \n",
       "2006-01-03 18:00:00         NaN         NaN         NaN         NaN   \n",
       "2006-01-04 00:00:00  127.349998  128.910004  126.379997  127.089996   \n",
       "\n",
       "                      Adj Close     Volume  \n",
       "Date                                        \n",
       "2006-01-03 00:00:00  112.337547  6188700.0  \n",
       "2006-01-03 06:00:00         NaN        NaN  \n",
       "2006-01-03 12:00:00         NaN        NaN  \n",
       "2006-01-03 18:00:00         NaN        NaN  \n",
       "2006-01-04 00:00:00  110.785889  4861600.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.resample(\"6H\").mean().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rolling / Expanding / EW"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These methods aren't unique to `DatetimeIndex`es, but they often make sense with time series, so I'll show them here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rGYAf3zg1/Bomg56bJ1+FUW/E5mrmkfVP86P3H+alXf9gb/XBPrns1OVu+1z+48rC8M87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89h0fIcXn9uLy1NvdhtLrSj+M7JjIwgCERMm0rEtKkAuG02zGXlWJuaMVdU0nOoyN+/3V/fQH99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eg7piotli8P82v8yJkmxMHgvxhb6K4eiJvTXvUEQ6NSWIIiIict7ONQexT3K3gPuumsA5iPHh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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114850048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gs.Close.plot(label='Raw')\n",
    "gs.Close.rolling(28).mean().plot(label='28D MA')\n",
    "gs.Close.expanding().mean().plot(label='Expanding Average')\n",
    "gs.Close.ewm(alpha=0.03).mean().plot(label='EWMA($\\\\alpha=.03$)')\n",
    "\n",
    "plt.legend(bbox_to_anchor=(1.25, .5))\n",
    "plt.tight_layout()\n",
    "plt.ylabel(\"Close ($)\")\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each of `.rolling`, `.expanding`, and `.ewm` return a deferred object, similar to a GroupBy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rolling [window=30,center=True,axis=0]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roll = gs.Close.rolling(30, center=True)\n",
    "roll"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AgK985Svo7u7G4cOH8dnPfhZ33XUXDh8+vJQvnRBV0YR6wJ7iBDzy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\nf7u/IjX4dnMmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1075d04e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m = roll.agg(['mean', 'std'])\n",
    "ax = m['mean'].plot()\n",
    "ax.fill_between(m.index, m['mean'] - m['std'], m['mean'] + m['std'],\n",
    "                alpha=.25)\n",
    "plt.tight_layout()\n",
    "plt.ylabel(\"Close ($)\")\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grab Bag\n",
    "\n",
    "### Offsets\n",
    "\n",
    "These are similar to `dateutil.relativedelta`, but works with arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2006-04-01', '2006-04-02', '2006-04-03', '2006-04-04',\n",
       "               '2006-04-07', '2006-04-08', '2006-04-09', '2006-04-10',\n",
       "               '2006-04-11', '2006-04-15',\n",
       "               ...\n",
       "               '2010-03-15', '2010-03-16', '2010-03-19', '2010-03-20',\n",
       "               '2010-03-21', '2010-03-22', '2010-03-26', '2010-03-27',\n",
       "               '2010-03-28', '2010-03-29'],\n",
       "              dtype='datetime64[ns]', name='Date', length=1007, freq=None)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.index + pd.DateOffset(months=3, days=-2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Holiday Calendars\n",
    "\n",
    "There are a whole bunch of special calendars, useful for traders probabaly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pandas.tseries.holiday import USColumbusDay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-10-12', '2016-10-10', '2017-10-09', '2018-10-08',\n",
       "               '2019-10-14'],\n",
       "              dtype='datetime64[ns]', freq='WOM-2MON')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "USColumbusDay.dates('2015-01-01', '2020-01-01')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Timezones\n",
    "\n",
    "Pandas works with `pytz` for nice timezone-aware datetimes.\n",
    "The typical workflow is\n",
    "\n",
    "1. localize timezone-naive timestamps to some timezone\n",
    "2. convert to desired timezone\n",
    "\n",
    "If you already have timezone-aware Timestamps, there's no need for step one. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-01-03 05:00:00+00:00</th>\n",
       "      <td>126.699997</td>\n",
       "      <td>129.440002</td>\n",
       "      <td>124.230003</td>\n",
       "      <td>128.869995</td>\n",
       "      <td>112.337547</td>\n",
       "      <td>6188700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-04 05:00:00+00:00</th>\n",
       "      <td>127.349998</td>\n",
       "      <td>128.910004</td>\n",
       "      <td>126.379997</td>\n",
       "      <td>127.089996</td>\n",
       "      <td>110.785889</td>\n",
       "      <td>4861600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-05 05:00:00+00:00</th>\n",
       "      <td>126.000000</td>\n",
       "      <td>127.320000</td>\n",
       "      <td>125.610001</td>\n",
       "      <td>127.040001</td>\n",
       "      <td>110.742340</td>\n",
       "      <td>3717400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-06 05:00:00+00:00</th>\n",
       "      <td>127.290001</td>\n",
       "      <td>129.250000</td>\n",
       "      <td>127.290001</td>\n",
       "      <td>128.839996</td>\n",
       "      <td>112.311401</td>\n",
       "      <td>4319600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-09 05:00:00+00:00</th>\n",
       "      <td>128.500000</td>\n",
       "      <td>130.619995</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>130.389999</td>\n",
       "      <td>113.662605</td>\n",
       "      <td>4723500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Open        High         Low       Close  \\\n",
       "Date                                                                        \n",
       "2006-01-03 05:00:00+00:00  126.699997  129.440002  124.230003  128.869995   \n",
       "2006-01-04 05:00:00+00:00  127.349998  128.910004  126.379997  127.089996   \n",
       "2006-01-05 05:00:00+00:00  126.000000  127.320000  125.610001  127.040001   \n",
       "2006-01-06 05:00:00+00:00  127.290001  129.250000  127.290001  128.839996   \n",
       "2006-01-09 05:00:00+00:00  128.500000  130.619995  128.000000  130.389999   \n",
       "\n",
       "                            Adj Close   Volume  \n",
       "Date                                            \n",
       "2006-01-03 05:00:00+00:00  112.337547  6188700  \n",
       "2006-01-04 05:00:00+00:00  110.785889  4861600  \n",
       "2006-01-05 05:00:00+00:00  110.742340  3717400  \n",
       "2006-01-06 05:00:00+00:00  112.311401  4319600  \n",
       "2006-01-09 05:00:00+00:00  113.662605  4723500  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tz naiive -> tz aware..... to desired UTC\n",
    "gs.tz_localize('US/Eastern').tz_convert('UTC').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling Time Series\n",
    "\n",
    "The rest of this post will focus on time series in the econometric sense.\n",
    "My indented reader for this section isn't all that clear, so I apologize upfront for any sudden shifts in complexity.\n",
    "I'm roughly targeting material that could be presented in a first or second semester applied statisctics course.\n",
    "What follows certainly isn't a replacement for that.\n",
    "Any formality will be restricted to footnotes for the curious.\n",
    "I've put a whole bunch of resources at the end for people earger to learn more.\n",
    "\n",
    "We'll focus on modelling Average Monthly Flights. Let's download the data.\n",
    "If you've been following along in the series, you've seen most of this code before, so feel free to skip."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import io\n",
    "import glob\n",
    "import zipfile\n",
    "from utils import download_timeseries\n",
    "\n",
    "import statsmodels.api as sm\n",
    "\n",
    "\n",
    "def download_many(start, end):\n",
    "    months = pd.period_range(start, end=end, freq='M')\n",
    "    # We could easily parallelize this loop.\n",
    "    for i, month in enumerate(months):\n",
    "        download_timeseries(month)\n",
    "\n",
    "\n",
    "def time_to_datetime(df, columns):\n",
    "    '''\n",
    "    Combine all time items into datetimes.\n",
    "\n",
    "    2014-01-01,1149.0 -> 2014-01-01T11:49:00\n",
    "    '''\n",
    "    def converter(col):\n",
    "        timepart = (col.astype(str)\n",
    "                       .str.replace('\\.0$', '')  # NaNs force float dtype\n",
    "                       .str.pad(4, fillchar='0'))\n",
    "        return  pd.to_datetime(df['fl_date'] + ' ' +\n",
    "                               timepart.str.slice(0, 2) + ':' +\n",
    "                               timepart.str.slice(2, 4),\n",
    "                               errors='coerce')\n",
    "        return datetime_part\n",
    "    df[columns] = df[columns].apply(converter)\n",
    "    return df\n",
    "\n",
    "\n",
    "def read_one(fp):\n",
    "    df = (pd.read_csv(fp, encoding='latin1')\n",
    "            .rename(columns=str.lower)\n",
    "            .drop('unnamed: 6', axis=1)\n",
    "            .pipe(time_to_datetime, ['dep_time', 'arr_time', 'crs_arr_time',\n",
    "                                     'crs_dep_time'])\n",
    "            .assign(fl_date=lambda x: pd.to_datetime(x['fl_date'])))\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "store = 'data/ts.hdf5'\n",
    "\n",
    "if not os.path.exists(store):\n",
    "    download_many('2000-01-01', '2016-01-01')\n",
    "\n",
    "    zips = glob.glob(os.path.join('data', 'timeseries', '*.zip'))\n",
    "    dfs = [read_one(fp) for fp in csvs]\n",
    "    df = pd.concat(dfs, ignore_index=True)\n",
    "\n",
    "    df['origin'] = df['origin'].astype('category')\n",
    "    df.to_hdf(store, 'ts', format='table')\n",
    "else:\n",
    "    df = pd.read_hdf(store, 'ts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fl_date         datetime64[ns]\n",
      "origin                category\n",
      "crs_dep_time    datetime64[ns]\n",
      "dep_time        datetime64[ns]\n",
      "crs_arr_time    datetime64[ns]\n",
      "arr_time        datetime64[ns]\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "with pd.option_context('display.max_rows', 100):\n",
    "    print(df.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can calculate the historical values with a resample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01    15176.677419\n",
       "2000-02-01    15327.551724\n",
       "2000-03-01    15578.838710\n",
       "2000-04-01    15442.100000\n",
       "2000-05-01    15448.677419\n",
       "Freq: MS, Name: fl_date, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily = df.fl_date.value_counts().sort_index()\n",
    "y = daily.resample('MS').mean()\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that I use the `\"MS\"` frequency code there.\n",
    "Pandas defaults to end of month (or end of year).\n",
    "Append an `'S'` to get the start."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AwOIrS8I63jJcPQezS8ExAIwfLQadpi5HRDKRUsCdYfBNE5CyndIkAiKiaIpp0Hn48GHc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\nppOIiIiIpGPTSURERETS/RfbVJDLbQmdbwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1216391d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = y.plot()\n",
    "ax.set(ylabel='Average Monthly Flights')\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.tsa.api as smt\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One note of warning: I'm using the development version of statsmodels (commit `de15ec8` to be precise).\n",
    "Not all of the items I've shown here are available in the currently-released version.\n",
    "\n",
    "Think back to a typical regression problem, ignoring anything to do with time series for now.\n",
    "The usual task is to predict some value $y$ using some a linear combination of features in $X$.\n",
    "\n",
    "$$y = \\beta_0 + \\beta_1 X_1 + \\ldots + \\beta_p X_p + \\epsilon$$\n",
    "\n",
    "When working with time series, some of the most important (and sometimes *only*) features are the previous, or *lagged*, values of $y$.\n",
    "\n",
    "Let's start by trying just that \"manually\": running a regression of `y` on lagged values of itself.\n",
    "We'll see that this regression suffers from a few problems: multicollinearity, autocorrelation, non-stationarity, and seasonality.\n",
    "I'll explain what each of those are in turn and why they're problems.\n",
    "Afterwards, we'll use a second model, seasonal ARIMA, which handles those problems for us.\n",
    "\n",
    "First, let's create a dataframe with our lagged values of `y` using the `.shift` method, which shifts the index `i` periods, so it lines up with that observation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>L1</th>\n",
       "      <th>L2</th>\n",
       "      <th>L3</th>\n",
       "      <th>L4</th>\n",
       "      <th>L5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-06-01</th>\n",
       "      <td>15703.333333</td>\n",
       "      <td>15448.677419</td>\n",
       "      <td>15442.100000</td>\n",
       "      <td>15578.838710</td>\n",
       "      <td>15327.551724</td>\n",
       "      <td>15176.677419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-07-01</th>\n",
       "      <td>15591.677419</td>\n",
       "      <td>15703.333333</td>\n",
       "      <td>15448.677419</td>\n",
       "      <td>15442.100000</td>\n",
       "      <td>15578.838710</td>\n",
       "      <td>15327.551724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-08-01</th>\n",
       "      <td>15850.516129</td>\n",
       "      <td>15591.677419</td>\n",
       "      <td>15703.333333</td>\n",
       "      <td>15448.677419</td>\n",
       "      <td>15442.100000</td>\n",
       "      <td>15578.838710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-09-01</th>\n",
       "      <td>15436.566667</td>\n",
       "      <td>15850.516129</td>\n",
       "      <td>15591.677419</td>\n",
       "      <td>15703.333333</td>\n",
       "      <td>15448.677419</td>\n",
       "      <td>15442.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-10-01</th>\n",
       "      <td>15669.709677</td>\n",
       "      <td>15436.566667</td>\n",
       "      <td>15850.516129</td>\n",
       "      <td>15591.677419</td>\n",
       "      <td>15703.333333</td>\n",
       "      <td>15448.677419</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       y            L1            L2            L3  \\\n",
       "2000-06-01  15703.333333  15448.677419  15442.100000  15578.838710   \n",
       "2000-07-01  15591.677419  15703.333333  15448.677419  15442.100000   \n",
       "2000-08-01  15850.516129  15591.677419  15703.333333  15448.677419   \n",
       "2000-09-01  15436.566667  15850.516129  15591.677419  15703.333333   \n",
       "2000-10-01  15669.709677  15436.566667  15850.516129  15591.677419   \n",
       "\n",
       "                      L4            L5  \n",
       "2000-06-01  15327.551724  15176.677419  \n",
       "2000-07-01  15578.838710  15327.551724  \n",
       "2000-08-01  15442.100000  15578.838710  \n",
       "2000-09-01  15448.677419  15442.100000  \n",
       "2000-10-01  15703.333333  15448.677419  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = (pd.concat([y.shift(i) for i in range(6)], axis=1,\n",
    "               keys=['y'] + ['L%s' % i for i in range(1, 6)])\n",
    "       .dropna())\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can fit the lagged model using statsmodels (which uses [patsy](http://patsy.readthedocs.org) to translate the formula string to a design matrix)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.896</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.893</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   261.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 03 Sep 2017</td> <th>  Prob (F-statistic):</th> <td>2.61e-86</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:09:03</td>     <th>  Log-Likelihood:    </th> <td> -1461.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   188</td>      <th>  AIC:               </th> <td>   2936.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   181</td>      <th>  BIC:               </th> <td>   2959.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     6</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td> 1055.4443</td> <td>  459.096</td> <td>    2.299</td> <td> 0.023</td> <td>  149.575</td> <td> 1961.314</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>trend</th>     <td>   -1.0395</td> <td>    0.795</td> <td>   -1.307</td> <td> 0.193</td> <td>   -2.609</td> <td>    0.530</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1</th>        <td>    1.0143</td> <td>    0.075</td> <td>   13.543</td> <td> 0.000</td> <td>    0.867</td> <td>    1.162</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2</th>        <td>   -0.0769</td> <td>    0.106</td> <td>   -0.725</td> <td> 0.470</td> <td>   -0.286</td> <td>    0.133</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L3</th>        <td>   -0.0666</td> <td>    0.106</td> <td>   -0.627</td> <td> 0.531</td> <td>   -0.276</td> <td>    0.143</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L4</th>        <td>    0.1311</td> <td>    0.106</td> <td>    1.235</td> <td> 0.219</td> <td>   -0.078</td> <td>    0.341</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L5</th>        <td>   -0.0567</td> <td>    0.075</td> <td>   -0.758</td> <td> 0.449</td> <td>   -0.204</td> <td>    0.091</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>74.709</td> <th>  Durbin-Watson:     </th> <td>   1.979</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 851.300</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.114</td> <th>  Prob(JB):          </th> <td>1.39e-185</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>13.184</td> <th>  Cond. No.          </th> <td>4.24e+05</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.896\n",
       "Model:                            OLS   Adj. R-squared:                  0.893\n",
       "Method:                 Least Squares   F-statistic:                     261.1\n",
       "Date:                Sun, 03 Sep 2017   Prob (F-statistic):           2.61e-86\n",
       "Time:                        11:09:03   Log-Likelihood:                -1461.2\n",
       "No. Observations:                 188   AIC:                             2936.\n",
       "Df Residuals:                     181   BIC:                             2959.\n",
       "Df Model:                           6                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept   1055.4443    459.096      2.299      0.023     149.575    1961.314\n",
       "trend         -1.0395      0.795     -1.307      0.193      -2.609       0.530\n",
       "L1             1.0143      0.075     13.543      0.000       0.867       1.162\n",
       "L2            -0.0769      0.106     -0.725      0.470      -0.286       0.133\n",
       "L3            -0.0666      0.106     -0.627      0.531      -0.276       0.143\n",
       "L4             0.1311      0.106      1.235      0.219      -0.078       0.341\n",
       "L5            -0.0567      0.075     -0.758      0.449      -0.204       0.091\n",
       "==============================================================================\n",
       "Omnibus:                       74.709   Durbin-Watson:                   1.979\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              851.300\n",
       "Skew:                           1.114   Prob(JB):                    1.39e-185\n",
       "Kurtosis:                      13.184   Cond. No.                     4.24e+05\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 4.24e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod_lagged = smf.ols('y ~ trend + L1 + L2 + L3 + L4 + L5',\n",
    "                     data=X.assign(trend=np.arange(len(X))))\n",
    "res_lagged = mod_lagged.fit()\n",
    "res_lagged.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a few problems with this approach though.\n",
    "Since our lagged values are highly correlated with each other, our regression suffers from [multicollinearity](https://en.wikipedia.org/wiki/Multicollinearity).\n",
    "That ruins our estimates of the slopes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DvQlSAADAVl6vV2VlZe3Wi4uLVVJS0un7T5061SYgSVJCQoJ8Pl+btZEjR2rFihW66qqr\n1LNnTy1atEgxMTFqampSY2OjunXr1ub1iYmJamxsDLk3QQoAANiqsLBQbre73XpycnKX3p+YmNgu\nNPl8PiUlJbVZu+eee3Ty5EkVFBQoLi5OxcXFWrt2rbp3737GazQ2Nra7xj8iSAEAANPCefyBy+Xq\n0givI/369ZPX622zVltb2y6cHT16VHfeeadmzJghSfrLX/6ilpYWpaamql+/fnrrrbeCr21padGh\nQ4fUv3//kHtzszkAAIhqOTk58vv9WrlypZqbm/XKK6+orq5Oubm5bV5XUVGh6dOn6+TJkzp+/Lh+\n+ctf6vbbb1dsbKxGjx6t3bt3q7KyUn6/X0uXLlXv3r01cODAkHsTpAAAgGmRdI5UXFycli1bpjVr\n1mjYsGHyer1aunSpkpKSVFRUpPLycklSUVGRvvvd7+r666/XLbfcon79+umRRx6RJKWkpGjJkiUq\nKytTdna2qqqqtHjx4k47b4z2AACAaZH2o8UDBgzQSy+91G59+fLlwX+Oj4/X/PnzO7zGNddcoz/9\n6U+m9qUjBQAAYBEdKQAAYBq/tWegIwUAAGARQQoAAMCikEGqublZzzzzjKZMmaL58+fr+PHjbZ6f\nPHnyWS0OAABEpkj61p6dQt4jVVpaql27dmns2LHasGGD8vPz5fV61adPH0nSJ598YnrDjn5PBwAA\nRA/ukTKEDFJvvfWWKioq1KNHDxUWFmrevHm66667tHr1an3nO9+xtGFHv6cz9/qplq4HAABgl5BB\nqrm5Wd27dw/++bHHHtN9992n++67T8uXL1cgEDC9YUe/p7O1tML0tQAAgD1oSBlC3iM1dOhQlZaW\n6ssvvwyuPfXUU/rmm29UUlJiKUi5XC6lpaW1ewAAgOjhdDjC9ohmIYPU7NmztW/fPs2aNSu4Fh8f\nr+XLl+vrr79WU1PTWS8QAAAgUoUc7fXq1Sv4A4B/78ILL9TKlSu1Y8eOs1ocAABAJOvSOVKxsbHt\n1o4fP64FCxaEvSAAAIBoYfknYpqbm7V9+/Zw1gIAAKJElN/aFDb81h4AADCNc6QM/EQMAACARSE7\nUjU1NR0+d+zYsbAXAwAAogMNKUPIIOV2u+VwODo8L4q2HgAA5ycygCFkkKqurj5XdQAAAEQd7pEC\nAACwiG/tAQAA05jsGehIAQAAWERHCgAAmBbtPzYcLgQpAABgGjnKwGgPAADAIjpSAADANM6RMtCR\nAgAAsIggBQAAYBGjPQAAYBqTPQNBCgAAmMY9UgZGewAAABbRkQIAAKbRkDIQpAAAgGmM9gyM9gAA\nACwiSAEAAFjEaA8AAJjGZM9ARwoAAMAiOlIAAMA0bjY3EKQAAIBp5CgDoz0AAACLIqYjNfDKi+0u\nIeoEAgG7S8B54sODO+wuAUAIg2zY00lLSlIEBSkAABA9yFEGRnsAAAAWEaQAAAAsYrQHAABM4/gD\nAx0pAAAAi+hIAQAA02hIGQhSAADANIeTJCUx2gMAALCMjhQAADCN0Z6BjhQAAIBFBCkAAACLGO0B\nAADTOEfKQEcKAADAIjpSAADANBpSBoIUAAAwjdGegdEeAACARXSkAACAaTSkDHSkAAAALCJIAQAA\nWMRoDwAAmMdsTxJBCgAAWMC39gyM9gAAACyiIwUAAEyjIWUgSAEAANMcTpKUxGgPAADAMoIUAACI\nenv27FF+fr4yMzM1btw47dy584yvW7JkifLy8nT11Vfrrrvu0uHDh4PPzZkzR4MGDdKQIUOCjyNH\njoTclyAFAABMczjC9/i2mpqa5PF4NH78eG3dulWTJ09WcXGx/H5/m9e9++67eu211/Tqq6+qqqpK\nl156qWbNmhV8fu/evVqwYIF27NgRfPTp0yfk3gQpAAAQ1TZv3iyn06mCggLFxsYqPz9fLpdLGzZs\naPO6zz77TK2trWptbVUgEFBMTIwSEhIkSa2trdq3b58yMjJM7c3N5gAAwLRwniNVX1+vhoaGduvJ\nyclyuVydvr+2tlbp6elt1tLS0rR//36NGTMmuHbrrbfq5Zdf1nXXXaeYmBhdfPHFWrVqlSQjZPl8\nPpWWlmr79u3q3bu3pk2bpuuvvz7k3p12pDZs2KDt27dLksrKyjRu3DhNnjxZFRUVnf7FAADAP6dw\njva8Xq9uuummdg+v19ulWk6dOqXExMQ2awkJCfL5fG3W/H6/srKy9Pbbb2vr1q3Kzc3VAw88oEAg\noBMnTmjYsGEqKirSBx98oOLiYt1///3at29fyL1DdqSWLFmiVatWKRAIaPjw4dq9e7eKiork9/tV\nVlamkydPqqCgoEt/yf/VUeoEAADnp8LCQrnd7nbrycnJXXp/YmJiu9Dk8/mUlJTUZm3evHkaPXq0\nLrvsMknSY489pqysLH366afKzMzUCy+8EHztDTfcoJycHL333nu64oorOtw7ZJBavXq1Vq9erS++\n+EI//elPVVlZqb59+0qSrr76ak2dOtV0kPJ6vSorK2u3/ur9vzB1HQAAYJ9wjvZcLleXRngd6dev\nX7vuVW1tbbtwduTIkTY3oDudTjmdTl1wwQXatGmTDh48qIkTJwafb2pqUnx8fMi9Qwapr7/+Wt/9\n7neVkpKiPn36qFevXsHnLr30UkudpY5S58m3Npm+FgAAQE5Ojvx+v1auXKmJEyeqoqJCdXV1ys3N\nbfO6ESNGaMWKFcrLy1OvXr309NNP6/LLL1daWprq6upUWlqq/v37a8iQIXrzzTf10Ucfaf78+SH3\nDhmkMjIy5PV6VVhYqPXr1wfXT5w4oUWLFikrK8v0X7aj1LlbBCkAAGBeXFycli1bpscff1wLFy5U\namqqli5dqqSkJBUVFWno0KHyeDwqKSnR6dOnVVBQELxf6tlnn5XT6VR2drZmzpypmTNn6ujRo0pL\nS1N5eXmbJtKZOAKBQKCjJ6urq/Xzn/9ca9euDX49UDLmhj179tSvf/1r9e7dOywfwu6l/y8s1zmf\nfLLzC7tLiEob9x60u4So8+HBHXaXEHVyU4fYXUJUui4j1e4SotIdv33gnO+59VfPh+1aVz8yJWzX\nOtdCdqQGDBig9evXt5uDrl69WhdddNFZLQwAAESucN4jFc06Pf7gTB/URRddpOPHj2vSpElnpSgA\nAIBoYPlAzubm5uD5UgAA4DzDb6NI4mRzAABgAaM9A3kSAADAopAdqZqamg6fO3bsWNiLAQAAiCYh\ng5Tb7ZbD4VBHJyTQ1gMA4PxEBDCEDFLV1dXnqg4AAICow83mAADANKZSBoIUAAAwjRxl4Ft7AAAA\nFtGRAgAA5tGSkkRHCgAAwDKCFAAAgEWM9gAAgGkOJ6M9iSAFAAAs4BYpA6M9AAAAi+hIAQAA0ziQ\n00BHCgAAwCI6UgAAwDQaUgY6UgAAABYRpAAAACxitAcAAMxjtieJIAUAACzgQE4Doz0AAACL6EgB\nAADTmOwZCFIAAMA8kpQkRnsAAACWEaQAAAAsipjRXsqgPnaXEHW+b3cBADr04cEddpeA88gdNuzJ\nZM9ARwoAAMCiiOlIAQCA6ME5UgaCFAAAMM3BbE8Soz0AAADL6EgBAADzaEhJoiMFAABgGUEKAADA\nIkZ7AADANG42NxCkAACAaQQpA6M9AAAAi+hIAQAA82jFSCJIAQAACxjtGciTAAAAFhGkAAAALGK0\nBwAATGO0Z6AjBQAAYBEdKQAAYB4NKUkEKQAAYIHDSZKSGO0BAABYRkcKAACYx83mkuhIAQAAWEaQ\nAgAAsIjRHgAAMI3JnoEgBQAATONATgOjPQAAAIvoSAEAAPM4R0oSQQoAAFjAaM/AaA8AAMAighQA\nAIBFloKUx+MJdx0AAABRJ+Q9Ur/61a/OuL5p06bgc4888oipDevr69XQ0NBuPcnUVQAAgK24RUpS\nJ0Fq165d2r59u2644QYlJf1f1GlpaVF9fb2lDb1er8rKytqtv7/8t5auBwAAzj1uNjeEDFIvvvii\nVqxYof/6r//S3LlzlZWVJUlav369nnzySUsbFhYWyu12t3/irwctXQ8AAMAuIYOUw+FQUVGRcnNz\n9eijj+raa6/Vfffd9602dLlccrlc7da/IEgBABA1HJwjJamLN5sPGDBAq1evlt/vV35+vlpaWs52\nXQAAIJI5HOF7RLEuf2svLi5Ojz76qGbMmKFbb731bNYEAABgyp49e5Sfn6/MzEyNGzdOO3fuPOPr\nlixZory8PF199dW66667dPjw4eBzVVVVcrvdyszMVEFBgWprazvd1/TxBzk5OXriiSd0/PhxTZo0\nyezbAQDAPwGHwxG2x7fV1NQkj8ej8ePHa+vWrZo8ebKKi4vl9/vbvO7dd9/Va6+9pldffVVVVVW6\n9NJLNWvWLElSXV2diouL9eCDD2rLli0aPny4HnrooU73tnwgZ3Nzs7Zv32717QAAAGGxefNmOZ1O\nFRQUKDY2Vvn5+XK5XNqwYUOb13322WdqbW1Va2urAoGAYmJilJCQIEmqrKxURkaGRo4cqbi4OE2d\nOlWHDx/W7t27Q+7Nb+0BAABbdXTGZHJy8hm/oPaPamtrlZ6e3mYtLS1N+/fv15gxY4Jrt956q15+\n+WVdd911iomJ0cUXX6xVq1ZJkg4cONDmGjExMerbt69qamo0aNCgDvfmJ2IAAIB5jvA9vF6vbrrp\npnYPr9fbpVJOnTqlxMTENmsJCQny+Xxt1vx+v7KysvT2229r69atys3N1QMPPKBAIKDGxsZ210hM\nTFRjY2PIvUN2pGpqajp87tixYyEvDAAA/nmF8/iDjs6YTE5O7tL7ExMT24Umn8/X5jBxSZo3b55G\njx6tyy67TJL02GOPKSsrS59++ukZr9HY2NjuGv8oZJByu91yOBwKBAJnfJ5TTQEAwLfV0RmTXdWv\nX7923ava2tp24ezIkSNtbkB3Op1yOp264IIL1K9fP7311lvB51paWnTo0CH1798/5N4hg1R1dXWX\n/xIAAOA8EkHNlJycHPn9fq1cuVITJ05URUWF6urqlJub2+Z1I0aM0IoVK5SXl6devXrp6aef1uWX\nX660tDR1795dCxYsUGVlpUaMGKHnnntOvXv31sCBA0PuzT1SAADAtEg6/iAuLk7Lli3TmjVrNGzY\nMHm9Xi1dulRJSUkqKipSeXm5JKmkpEQ33nijCgoKlJeXp0OHDunZZ5+V0+lUSkqKlixZorKyMmVn\nZ6uqqkqLFy/utD5HoKO53Tn2xQfv2V1C1Dm2+4jdJUSlT3Z+YXcJUWfjXn7CyawPD+6wu4SolJs6\nxO4SotKS9399zvc8sm5d2K7VZ/TosF3rXKMjBQAAYBHnSAEAAPP40WJJdKQAAAAsoyMFAABM4wgk\nA0EKAACYR46SxGgPAADAMjpSAADANEZ7BjpSAAAAFhGkAAAALGK0BwAAzOMcKUkEKQAAYAH3SBkY\n7QEAAFhERwoAAJhHR0oSQQoAAFjAaM/AaA8AAMAighQAAIBFjPYAAIB5HH8giY4UAACAZRHTkXJd\nOdjuEnCe+L7dBQDo0IcHd9hdArqIm80NEROkAABAFCFISWK0BwAAYBkdKQAAYJqDm80l0ZECAACw\njCAFAABgEaM9AABgHjebS6IjBQAAYBkdKQAAYBrnSBkIUgAAwDyClCRGewAAAJbRkQIAAKZxjpSB\njhQAAIBFBCkAAACLGO0BAADzuNlcEkEKAABYQZCSxGgPAADAMjpSAADANA7kNBCkAACAeRx/IInR\nHgAAgGUEKQAAAIsY7QEAANMcDnoxEh0pAAAAy+hIAQAA8/jWniSCFAAAsIDjDwyM9gAAACyiIwUA\nAMzjHClJdKQAAAAsI0gBAABYxGgPAACYxs3mhpAdqeeeey74z83NzVq4cKFuvvlm3XbbbVq5cuVZ\nLw4AAEQohyN8jygWsiNVXl6ue+65R5K0cOFC/fnPf9YDDzwgn8+n3/72t/rqq69UXFxsasP6+no1\nNDS0W7+kR3dT1wEAALBbyCAVCASC//z222/rxRdf1Pe+9z1J0pVXXqlJkyaZDlJer1dlZWXt1ndt\nrTJ1HQAAYCN+IkZSJ0Hq7+efDodDPXv2DP75kksukc/nM71hYWGh3G636fcBAIDI4eD4A0mdBCmf\nzyePx6NpmyrSAAAJxElEQVSMjAxdcskleumllzRlyhSdOnVKixcv1uDBg01v6HK55HK52q37T3xp\n+loAAAB2CtmXe/nllzVq1Cg1NDTI7/dry5YtkqSysjK98847euyxx85JkQAAAJEoZEdq8ODBZ+w6\neTweTZ8+na8+AgBwviIDSLJ4IGf37t1VX1+vSZMmhbseAACAqGH5QM7m5mZt3749nLUAAIAowVTK\nwMnmAADAPI4/kMRv7QEAAFgWsiNVU1PT4XPHjh0LezEAACA6cI6UIWSQcrvdcjgcbU44/3vMRwEA\nwPksZJCqrq4+V3UAAABEHW42BwAA5jGVkkSQAgAAFnB7j4Fv7QEAAFhERwoAAJgXYedI7dmzR7Nn\nz1ZNTY1SU1M1Z84cZWZmtnlNUVGR/ud//if459bWVvl8Pq1atUpZWVmaM2eO/vCHPyg2Njb4mjVr\n1qhPnz4d7kuQAgAA5kXQ8QdNTU3yeDzyeDyaMGGCKioqVFxcrHfffVdxcXHB1y1fvrzN+2bMmKHT\np08rKytLkrR3714tWLBAN910U5f3jqw4CQAAYNLmzZvldDpVUFCg2NhY5efny+VyacOGDR2+5513\n3tHmzZs1Z84cSUZ3at++fcrIyDC1N0EKAABEtdraWqWnp7dZS0tL0/79+8/4+tOnT+vJJ5/UjBkz\n1K1bN0nSZ599Jp/Pp9LSUl1zzTW67bbbQgax/8VoDwAA2Kq+vl4NDQ3t1pOTk+VyuTp9/6lTp5SY\nmNhmLSEhQT6f74yvX7t2reLj49uM8E6cOKFhw4apqKhIgwcP1saNG3X//fdr9erVuuKKKzrcmyAF\nAABMC+fxB16vV2VlZe3Wi4uLVVJS0un7ExMT24Umn8+npKSkM77+j3/8o37yk5/I6fy/wVxmZqZe\neOGF4J9vuOEG5eTk6L333iNIAQCAMAvjt/YKCwvldrvbrScnJ3fp/f369ZPX622zVltbe8ZrfvPN\nN9q6datKS0vbrG/atEkHDx7UxIkTg2tNTU2Kj48PuTdBCgAA2MrlcnVphNeRnJwc+f1+rVy5UhMn\nTlRFRYXq6uqUm5vb7rW7d+/WxRdfrF69erVZdzqdKi0tVf/+/TVkyBC9+eab+uijjzR//vyQexOk\nAACAaZF0snlcXJyWLVumxx9/XAsXLlRqaqqWLl2qpKQkFRUVaejQofJ4PJKkv/3tb0pJSWl3jezs\nbM2cOVMzZ87U0aNHlZaWpvLy8naB6x85AoFA4Kz8rUzyn/jS7hKiTv1Hu+wuISod233E7hKizic7\nv7C7hKizce9Bu0uISh8e3GF3CVHp44Mbz/mevi8/D9u1Enr0Dtu1zjWOPwAAALCIIAUAAGAR90gB\nAADTHBH0EzF2oiMFAABgER0pAABgXgR9a89OBCkAAGCaI4wHckYzPgUAAACL6EgBAADzGO1JiqAD\nOSNVfX29vF6vCgsLv9Xx9ecTPjNr+NzM4zMzj8/MGj43dITRXicaGhpUVlamhoYGu0uJGnxm1vC5\nmcdnZh6fmTV8bugIQQoAAMAighQAAIBFBCkAAACLCFIAAAAWxTz++OOP211EpEtISNCwYcOUmJho\ndylRg8/MGj438/jMzOMzs4bPDWfC8QcAAAAWMdoDAACwiCAFAABgEUEKAADAIoIUAACARQQpAAAA\niwhSAAAAFhGkAAAALCJI4Vu54oor9Omnn4Z8TX19vUaNGtXp684XoT6zzz//XD//+c+VnZ2tH/7w\nh5o7d678fv85rjAyhfrcqqurNWnSJGVlZenaa6/Vs88+K47I69r/PltbWzV58mSVlpaeo6oiX6jP\n7eOPP1ZGRoaGDBkSfJSXl5/jChFJCFI4q7Zt26aCggL99a9/tbuUqDB9+nT17t1b77//vl577TXt\n2rVLzz77rN1lRbTW1lZNnTpVN954o7Zt26aXXnpJq1at0rvvvmt3aVHhd7/7nbZt22Z3GVGjurpa\n1157rXbs2BF8eDweu8uCjQhSHfj3f/93/cd//Efwzy0tLRo+fLg+/vhjG6uKLtu2bdO0adN07733\n2l1KVPD7/UpMTNTUqVMVHx+vlJQUjR07Vjt27LC7tIjmdDq1Zs0a/cu//ItaWlp09OhRtba26sIL\nL7S7tIhXXV2tP/7xjxo9erTdpUSNPXv2aMCAAXaXgQhCkOrAj370I1VWVur06dOSpKqqKnXr1k0/\n+MEPbK4selx++eVav369brvtNrtLiQpxcXF67rnnlJKSElzbsGED/9HugqSkJDkcDo0ZM0Z33HGH\nhg8frqysLLvLimh+v18zZszQE088oaSkJLvLiRp79+7V9u3bNXLkSI0YMUKlpaWM389zBKkOZGdn\nKy4uTlVVVZKkNWvWaOzYsTZXFV0uvPBCJSQk2F1GVAoEApo3b54OHDhAR8+EN998U+vWrdMnn3zC\nSLQTTz/9tHJzczV06FC7S4kqLpdLI0eO1BtvvKGVK1fqz3/+s37zm9/YXRZsRJDqgNPplNvt1ptv\nvqmmpia98847crvddpeF84DP59O0adP0wQcfaOXKlerRo4fdJUWN+Ph4XXrppSoqKlJlZaXd5USs\nTZs2afPmzZo2bZrdpUSd8vJy3XnnnUpKSlLfvn117733at26dXaXBRsRpEIYO3as1q9fr/fff1+X\nXXaZ0tLS7C4J/+QaGhpUWFiohoYGvfzyy+rbt6/dJUW848ePa9SoUWpoaAiuNTc3q3v37jZWFdnW\nrl2rQ4cOafjw4Ro6dKjeeOMNeb1eup+d+Oqrr1RaWqpvvvkmuNbU1KT4+Hgbq4LdLrC7gEg2cOBA\npaSkqKysTOPHj7e7nIj15Zdf6vPPPw/+OS4uThdddJGNFUW+M31mLpdLJSUl6tmzpxYvXqzY2Fgb\nK4xMHf271qNHDy1atEizZs3S4cOHtXz5cv3bv/2bjZVGjjN9ZnPnztXcuXODa48++qhcLpdmzJhh\nR4kR6UyfW3JystatW6dAIKCHHnpIR44cUXl5uX7yk5/YWCnsRpDqxNixY/Wb3/xGt9xyi92lRKwp\nU6a0+XNWVpZWrVplTzFR4kyf2fTp07VlyxbFx8dr2LBhwecGDhyo//zP/zzHFUamjv5de+aZZzRn\nzhz98Ic/1IUXXqgpU6boxz/+sT1FRhj+92lNR59beXm55s2bp2uuuUYJCQm644479LOf/cyeIhER\nHAFOrQvpT3/6kyoqKrRixQq7SwEAABGGe6Q68PXXX6u6ulq/+93vNGHCBLvLAQAAEYgg1YHa2lpN\nnDhR6enpGjNmjN3lAACACMRoDwAAwCI6UgAAABYRpAAAACwiSAEAAFhEkAIAALCIIAUAAGARQQoA\nAMCi/w8pT0KP9Nz3HAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x125a14a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(X.corr());"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Second, we'd intuitively expect the $\\beta_i$s to gradually decline to zero.\n",
    "The immediately preceding period *should* be most important ($\\beta_1$ is the largest coefficient in absolute value), followed by $\\beta_2$, and $\\beta_3$...\n",
    "Looking at the regression summary and the bar graph below, this isn't the case (the cause is related to multicollinearity)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b60b4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = res_lagged.params.drop(['Intercept', 'trend']).plot.bar(rot=0)\n",
    "plt.ylabel('Coefficeint')\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, our degrees of freedom drop since we lose two for each variable (one for estimating the coefficient, one for the lost observation as a result of the `shift`).\n",
    "At least in (macro)econometrics, each observation is precious and we're loath to throw them away, though sometimes that's unavoidable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Autocorrelation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another problem our lagged model suffered from is [autocorrelation](https://en.wikipedia.org/wiki/Autocorrelation) (also know as serial correlation).\n",
    "Roughly speaking, autocorrelation is when there's a clear pattern in the residuals of your regression (the observed minus the predicted).\n",
    "Let's fit a simple model of $y = \\beta_0 + \\beta_1 T + \\epsilon$, where `T` is the time trend (`np.arange(len(y))`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# `Results.resid` is a Series of residuals: y - ŷ\n",
    "mod_trend = sm.OLS.from_formula(\n",
    "    'y ~ trend', data=y.to_frame(name='y')\n",
    "                       .assign(trend=np.arange(len(y))))\n",
    "res_trend = mod_trend.fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Residuals (the observed minus the expected, or $\\hat{e_t} = y_t - \\hat{y_t}$) are supposed to be [white noise](https://en.wikipedia.org/wiki/White_noise).\n",
    "That's [one of the assumptions](https://en.wikipedia.org/wiki/Gauss–Markov_theorem) many of the properties of linear regression are founded upon.\n",
    "In this case there's a correlation between one residual and the next: if the residual at time $t$ was above expectation, then the residual at time $t + 1$ is *much* more likely to be above average as well ($e_t > 0 \\implies E_t[e_{t+1}] > 0$).\n",
    "\n",
    "We'll define a helper function to plot the residuals time series, and some diagnostics about them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def tsplot(y, lags=None, figsize=(10, 8)):\n",
    "    fig = plt.figure(figsize=figsize)\n",
    "    layout = (2, 2)\n",
    "    ts_ax = plt.subplot2grid(layout, (0, 0), colspan=2)\n",
    "    acf_ax = plt.subplot2grid(layout, (1, 0))\n",
    "    pacf_ax = plt.subplot2grid(layout, (1, 1))\n",
    "    \n",
    "    y.plot(ax=ts_ax)\n",
    "    smt.graphics.plot_acf(y, lags=lags, ax=acf_ax)\n",
    "    smt.graphics.plot_pacf(y, lags=lags, ax=pacf_ax)\n",
    "    [ax.set_xlim(1.5) for ax in [acf_ax, pacf_ax]]\n",
    "    sns.despine()\n",
    "    plt.tight_layout()\n",
    "    return ts_ax, acf_ax, pacf_ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calling it on the residuals from the linear trend:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XkCA4cKoddocLWo0Kl48RRXY+0qiV4ja5/SfbvB7bcqAJTe39APjuLokc7NCa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\nshebfidHzgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c026828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tsplot(res_trend.resid, lags=36);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The top subplot shows the time series of our residuals $e_t$, which should be white noise (but it isn't).\n",
    "The bottom shows the [autocorrelation](https://www.otexts.org/fpp/2/2#autocorrelation) of the residuals as a correlogram.\n",
    "It measures the correlation between a value and it's lagged self, e.g. $corr(e_t, e_{t-1}), corr(e_t, e_{t-2}), \\ldots$.\n",
    "The partial autocorrelation plot in the bottom-right shows a similar concept.\n",
    "It's partial in the sense that the value for $corr(e_t, e_{t-k})$ is the correlation between those two periods, after controlling for the values at all shorter lags.\n",
    "\n",
    "Autocorrelation is a problem in regular regressions like above, but we'll use it to our advantage when we setup an ARIMA model below.\n",
    "The basic idea is pretty sensible: if your regression residuals have a clear pattern, then there's clearly some structure in the data that you aren't taking advantage of.\n",
    "If a positive residual today means you'll likely have a positive residual tomorrow, why not incorporate that information into your forecast, and lower your forecasted value for tomorrow?\n",
    "That's pretty much what ARIMA does."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's important that your dataset be stationary, otherwise you run the risk of finding [spurious correlations](http://www.tylervigen.com/spurious-correlations).\n",
    "A common example is the relationship between number of TVs per person and life expectancy.\n",
    "It's not likely that there's an actual causal relationship there.\n",
    "Rather, there could be a third variable that's driving both (wealth, say).\n",
    "[Granger and Newbold (1974)](http://wolfweb.unr.edu/homepage/zal/STAT758/Granger_Newbold_1974.pdf) had some stern words for the econometrics literature on this.\n",
    "\n",
    "> We find it very curious that whereas virtually every textbook on econometric methodology contains explicit warnings of the dangers of autocorrelated errors, this phenomenon crops up so frequently in well-respected applied work.\n",
    "\n",
    "(:fire:), but in that academic passive-aggressive way.\n",
    "\n",
    "The typical way to handle non-stationarity is to difference the non-stationary variable until is is stationary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4z/9ezXtPzeOhmyfoezyeXuy2/QgdczKjFACzycDfVszkiXumEBHii0OBt5wT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C1N9ZG/CEj5wDMHSZG8T09v4+67TlGAEm+K5dfgg6Xe+7C6/w07CkvbnHbuPkcr7rksJG112tBs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\nIQk5jARBEARBEIQk5DASBEEQBEEQkvwfS+n0VDdNN5EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1258db978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y.to_frame(name='y').assign(Δy=lambda x: x.y.diff()).plot(subplots=True)\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our original series actually doesn't look *that* bad.\n",
    "It doesn't look like nominal GDP say, where there's a clearly rising trend.\n",
    "But we have more rigorous methods for detecting whether a series is non-stationary than simply plotting and squinting at it.\n",
    "One popular method is the Augmented Dickey-Fuller test.\n",
    "It's a statistical hypothesis test that roughly says:\n",
    "\n",
    "$H_0$ (null hypothesis): $y$ is non-stationary, needs to be differenced\n",
    "\n",
    "$H_A$ (alternative hypothesis): $y$ is stationary, doesn't need to be differenced\n",
    "\n",
    "I don't want to get into the weeds on exactly what the test statistic is, and what the distribution looks like.\n",
    "This is implemented in statsmodels as [`smt.adfuller`](http://www.statsmodels.org/dev/generated/statsmodels.tsa.stattools.adfuller.html).\n",
    "The return type is a bit busy for me, so we'll wrap it in a `namedtuple`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from collections import namedtuple\n",
    "\n",
    "ADF = namedtuple(\"ADF\", \"adf pvalue usedlag nobs critical icbest\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('adf', -1.3206520699512339),\n",
       "             ('pvalue', 0.61967180643147923),\n",
       "             ('usedlag', 15),\n",
       "             ('nobs', 177),\n",
       "             ('critical',\n",
       "              {'1%': -3.4678453197999071,\n",
       "               '10%': -2.575551186759871,\n",
       "               '5%': -2.8780117454974392}),\n",
       "             ('icbest', 2710.6120408261486)])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ADF(*smt.adfuller(y))._asdict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we failed to reject the null hypothesis that the original series was non-stationary.\n",
    "Let's difference it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('adf', -3.6412428797327996),\n",
       "             ('pvalue', 0.0050197770854934548),\n",
       "             ('usedlag', 14),\n",
       "             ('nobs', 177),\n",
       "             ('critical',\n",
       "              {'1%': -3.4678453197999071,\n",
       "               '10%': -2.575551186759871,\n",
       "               '5%': -2.8780117454974392}),\n",
       "             ('icbest', 2696.3891181091631)])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ADF(*smt.adfuller(y.diff().dropna()))._asdict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks better.\n",
    "It's not statistically significant at the 5% level, but who cares what statisticins say anyway.\n",
    "\n",
    "We'll fit another OLS model of $\\Delta y = \\beta_0 + \\beta_1 L \\Delta y_{t-1} + e_t$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = (y.to_frame(name='y')\n",
    "         .assign(Δy=lambda df: df.y.diff())\n",
    "         .assign(LΔy=lambda df: df.Δy.shift()))\n",
    "mod_stationary = smf.ols('Δy ~ LΔy', data=data.dropna())\n",
    "res_stationary = mod_stationary.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9A6LVMRbPXafL12PefKAgwdvPsAskVXF2mfAlh7dnYpYlS9ANEEeiTKT2FpfH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\nKM5S1uRxdocFuAITQeFpPl+Kg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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c02c860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tsplot(res_stationary.resid, lags=24);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we've taken care of multicolinearity, autocorelation, and stationarity, but we still aren't done."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seasonality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have strong monthly seasonality:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dttZUkWERrhf97Bg32JP//JSg/9xILGbsIFFJ52qjataQmV+DsknNgCAn5DIp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M/T4fyumjgYV/Fzar8i8XNIP7XqPKvvZ8mJVdfP30f8lPpdvI28+KmDKkH2okWLWLVqFRs2bODE\niROcPHmSdevW8fTTT3Pbbbep0YTD0ri8zMUE+XsxKaYfAAEdyNeFhp6WWqvXxU/8oGOLMrvbL9Df\nk0kx/cXPmf1KLYu6xM+R/FTr5Kntp3Yn1OIX2DxVQPzEr6v9OoIqJfzuvvtuqqurefPNNykpKQEg\nNDSUZcuWcdddd6nRhM2cOXOGp556iuTkZIYNG8bq1asZP368ase3TOcGt3ARANw+ZySZeRVce+mw\nDh3POlKiVk9Q/MQvr4Lrpjmm321zRpCRVy5+TupnLS/o5H7X9jA/tTt56vl1TSf04lQC8RO/7vDr\nCKoE2QAPPvggDz74IMXFxXh4eODn56fWoW2mtraWZcuWsWzZMm655Ra2bt3KI488wo4dO1TbJKet\nkSaAEUODeO/Pczp8PNVz6sRPEeKnDPFThqP7FbUx0gni1x5d56duJ081P2+V022sI53iZwvi1/Wo\nVid7x44dFBQU0LdvX77++mvuvvtuXnrpJXQ6nVpNdJr9+/fj4uLCokWLcHd3Z+HChQQFBbFz507V\n2mhvpKmzqL3jlPgpQ/yUIX7KcHS/9kY6O4v4KUPtHe2cxS8koHf5qVXnWfy6HlWC7LfeeovHH3+c\nCxcucPDgQVavXk14eDi7du2y626QaWlpREVFNbktIiKixUWaF1NSUkJaWlqTn8zMzGaPa2+kqbNY\npvvq9Ebq9AbFx+ttfpaeqvjZhvgpo7f5FYmfInqbn9rfH0XtpBN0Fmfx0xuM1k2olCB+XY8qQfaW\nLVt47bXXGD9+PF9++SWTJk1i9erVPP/883z77bdqNGET1dXVeHs3fXG8vLzQarWtPKOB9evXc911\n1zX5WbJkSZPH6OoMlFeZR+pV+5Cs72mB8t5Wb/TzET9FiJ8yeptfWaX42Yr4qein8kg7OIGfwtkU\n8euelBFVguzi4mJGjBgBwK5du7jqqqsACAgIsGu6iLe3d7OAWqvV4uPj0+5zFy9ezHfffdfk56OP\nPmrymKKylgulK8HHuyFNXmneUG/0a9wJEL/OI37K6E1+xa1sFKEE8VNGr/XrgkEkx/dT1gkQv+5J\nGVFl4WNUVBSbN28mODiYwsJCZs+ejVar5b333mP06NFqNGETkZGRrF+/vsltaWlpzJs3r93nBgUF\nERQU1OQ2d3f3Jn9bpnKhay4CpT2t3ujXeEMY8es84qeMXuXXxkYRtiJ+yui9fmqNdDqRn8IKN+LX\nPSPZqgTZK1eu5JFHHqGsrIwHHniA8PBwVq9eze7du3n33XfVaMImpk+fjk6nY926ddx+++1s3bqV\nwsJCZsyYocrxi+ovAl9vd7w91SnU0ng6Q+nWvb3Rz1InFsTPFsRPGb3Jr7B+pF38bEP8VPLzcmvy\nvaQEp/JT3IkXv+5AlXSRKVOmsG/fPg4cOMAf/vAHAB5++GF27txJbGysGk3YhIeHB++99x5ff/01\nU6dOZf369axdu7ZD6SIdwXIRqLXyFcDdzQUPN/PLonwkrPf5ubm6WHfWEr/OI37K6E1+lk6A+NmG\n+KnjF9xKeTdbcC4/dTrx4te1qFYnu6CggNTUVAwG84pNk8mETqfj9OnTLF++XK1mOk1MTAwbN27s\nkmNbLwKVptIs+Hi7o6uoVZ7T2Uv9fL3c0NUZxM9GxE8ZvcWvvY0ibEX8lNHb/NRalGnBafyUpmNY\nXt8+6nWioPf4dRRVguwNGzbw3HPPYTQa0Wg0mEwmADQaDePGjbNrkN2VNHwIqX8RlFbUKu5p9VY/\nHy93SsTPZsRPGb3Fz7IwU/xsQ/zEzxbU9mttoxdb6S1+HUWVdJEPPviAhx56iJMnTxIcHMyuXbvY\ntm0bMTExzJnT8d14nI3CLrwIQPnWvb3XT52tj8VP/Gyh1/hZ0gnEzyZ6q5911z3xswm1di0s7KqZ\nsl7i11FUCbLz8/NZsGAB7u7uxMbGcvz4caKjo1m5ciWbN29WowmHpOumcy27iqmU0yR+NiF+4mcL\nvcavy9IJxE8Jju7nI36KUGvXUetGQyqVZ7TQW/w6iipBdmBgIBUVFYB5R8XExEQABg0aRG5urhpN\nOBx6g5HSylqgCy4CFXpa4id+tiJ+yugtfiUV4mcrvdnP0gkQP9uw+imoftLYr6s6eT3ZrzOoEmRf\nffXVPPXUUyQkJDBt2jS2bt3K0aNHWbduHQMGDFCjCYejuFxLfep5FyxsUN7TEj/xsxXxU0Zv8Csp\nrxU/8bMJSzqL+NmG6n5dlK7Uk/06gypB9ooVK4iJiSEhIYFZs2YxdepUFi9ezJYtW1ixYoUaTTgc\nRaWNd8Pqqpxn23ta4id+tiJ+yugVfo03yhG/TtOb/Sy1isXPNqx+CoLExn5qlmiE3uHXGVSpLuLr\n68uaNWusf7/wwgusWrUKT09P3NxUqxLoUFhW/nt5uOLrpe7/6KtCT0v8xM9WxE8Z4qcM8VOGo/up\nkRMrfspyxi1+nh6uTTZoUYPe4NcZVBnJBkhKSmLVqlXceeed5OXl8b///Y8DBw6odXiHo/HKV41G\no+qxfVToaYmf+NmK+ClD/JQhfspwdD9L0Cl+tmH1U1D9xFr5JMBL/LoYVYLsffv2sXDhQqqrqzl+\n/Dg6nY78/Hzuv/9+vvnmGzWacDiSs0oBGBjqq/qxG0YibO9piZ/42Yr4KaNX+GWWAeJnK73ZT42R\nRGfxGxDSdX7Vqvj5qeLUmN7g1xlUmcd49dVXeeKJJ1i8eDETJkwA4LHHHqNv37689dZbXH/99Wo0\n4zCYTCbikwoAGBsdqvrxldahNplMnEgqBGBsdIhqXhbETxnipwzxU4YafvHJls8/8essvd1PaR1q\n8VOWDiR+TrjwMSkpiZkzZza7ffbs2WRmZqrRhEORVVBJWaUOgPEj1A+yLavDdXojOYVVlFbUUqc3\ndPj553MrrOW/xg0XP/ETP/FTzy8jt4LSCvETP9vw6UV+XREfWPzqxM8ufp1FlZHs/v37k5iYyJAh\nQ5rcvn///h5Zwu9sWjEAgX6eDAvzV/34ljqnAPc//yMA3p6urFl2OSOGBrX7/OPnChr59RE/8RM/\n8VPPr34WL8DPQ/zEr9NYOgHiJ37O6NdZVBnJvv/++1m1ahUffvghRqORPXv28OKLL7JmzRruu+8+\nNZpwKM6kFQHmXrTaSfkAQ/r7ExrUtOxPTa2BuBPZHXp+QypLCC4u4id+4id+Tf36qeA3LjpU/MSv\n0wzu79cr/MaKX4/06yyqjGT/5je/ISQkhPfeew9vb29ef/11IiMjeeWVV7jmmmvUaMKhOJdRCu4B\njB+hfr4QgJeHG++suIbici26OgMbvk/gl/hs0nPK232u3mDkdKo5n3NcF0y1iF/X+aU5qN8n3yew\nV/x6lN+/FPidSqnPF++CqXrxE7+e4tcVqSziZ18/W1A0kl1bW8v27duprq5m5syZrF+/nocffpiJ\nEycSGRnJ4MGD1fJ0KHR15vydrnqTA7i7udC/rw9D+vszJjIYgLTs9i+Ccxkl1NSa/brqIhW/rvFL\nzy4TP/FzaL+kjFKrX1fkS4qf+PUUv3HDu2YQTvzs52cLNgfZ2dnZzJ07l8cff5yCAvPw/osvvsjf\n/vY3XF1dMRgM3HHHHZw8eVI1WUdiUKgv/YJ8uqWtiIEBgHmr5bL6BVGtEV+fzzkg2Jf+fcUPnMmv\n1iH9wsVPET3Jz5IvKX4NiJ8yeqJfWLAPYcHql+9rCfFTRmf8bMHmIPv1118nIiKCffv2MWzYMIqL\ni/n444+ZM2cOb731Fn//+9954IEHeP3119X0dRi6cpTpYoYNaEj+by/lwXKRju3CXuDF9CS/+GTL\nVKSd/NoZbbeHX7j4KaJH+dnh/St+yhA/Zdji153xgfgpozN+tmBzkL13715+//vf4+dnLhb+888/\nYzAYuOmmm6yPueKKKzh27JhySwekK6eqLsbP292aqN9WkFhTqyfxfAkgfo3pjF9CurlyjL382soL\ns5efr7c7/epHjcSv8/Qkv8Tz4ncx4qeMnujXnUGi+Cmjo362YnOQXV5eTkhIQ2/ywIEDuLq6Mm3a\nNOttfn5+GI1GZYYOiEYDl0R1X08aIHyAeUojrY28odOpRRiMJvFrAWfxS89xTL+I+t6++NlGT/HT\nG0yA+F2M+Cmjp/l1xSYqbeEsfu19/zqyn63YHGQPHDiQtLQ0AAwGA3v27GHy5Mn4+DTkSR04cECV\nxY833HAD48aNY8KECUyYMIEbbrjBel9cXBzz5s1j/PjxLFq0yOoEcObMGRYuXMj48eNZsGABx48f\nV+wC5ukFPx8PVY7VUSIGWt5Erfe0LFMtEQMDCPDz7BYvC+KnDItfW4sz7ekXLn6K6El+kYPE72LE\nTxk9ys/R37929OvI96+j+hWU1th0bJuD7Jtvvpk1a9bwzTff8Mwzz1BYWMhvf/tb6/2HDx/mtdde\nY+7cubY2AYBWqyUtLY2dO3dy7Ngxjh07xtdffw1AYWEhjzzyCI899hgHDx7ksssu4/HHHwfMlU+W\nLVvGr3/9aw4dOsSdd97JI488gk6nU+QDEBsRrPgYncVyEWTkVmAwtDw7YNnEYnw3TrVY6Al+lje5\n+DUnon6kXfxsoyf5dedUrgXxU4b4KaNTft2YymLBmfz0Tur3r8/jbTq2zUH20qVLufrqq1m9ejU/\n/vgjjz32GNdeey0Azz77LIsXL2bixIksXbrU1iYAOHfuHCEhIfTt27fZfdu3byc2NpZZs2bh4eHB\ngw8+SGZmJqdOnWL//v24uLiwaNEi3N3dWbhwIUFBQezcuVORD8DsKUPaf5DKWCpQ1OmNXCiobHZ/\naUWttRdmlw+hHuBnGQWwp5/eYCTLIf3MnQDxsw1n9yurbPCzSydA/BQhfsrojF9Xlp5rDWfya+n7\n19H9isu1nM+tsOnYNgfZrq6u/OlPf+LAgQPs27eP+++/33rfrbfeyv/+9z/++c9/4u7u3sZRzOj1\nesrLy5v9VFZWcubMGdzc3LjtttuYNm0a9957LykpKQCkpqYSFRXVxGnIkCEkJyeTlpbW5D6AiIgI\nkpKSOvT/lZSUkJaW1uQnMzMTgADf7p3KAAgL9sXTwxVoeUroRLK5F+jm6sKoiOYdkq7G2f1O1ld1\ncAS/llY429uvv/gpwtn9TiRZ/DTi1wLip4ye5DfaDjPd7X1/OJJfi/GBw/sV2HxsVbZVv5iRI0cS\nGxvb4ccfPHiQKVOmNPu58cYbAbjkkkt45ZVX2LVrF2PGjGHp0qVotVpqamrw9m66Paa3tzc1NTVU\nV1c3u8/LywutVtshp/Xr13Pdddc1+VmyZEmH/ye1cXXRMCzMH2g5OT++/iKNCQ/Cy1OVjTw7hbP7\nWUoLil/LiJ8ynN0vPtni11f8WkD8lNFT/EYOs4+fi4uG8LDWF+85kl9Lm744vF99/GIL3f/ftMBl\nl11GYmJiq/fffvvt1t//8Ic/sGHDBs6ePYu3t3ezoLmmpgYfHx+0Wm2z+7RabZOFmW2xePFi5s2b\n1+S23NxcuwbaEQMDOJdR2iw532QycfxcPmCfqTQLzuoH9s2HtSB+yhA/ZbTlZ1lPYY9UFgvipwzx\nU0ZH/LqztODFhA/sQ2JGicP7tVQmz5H9TCaTdRDEFrpkJFtNNm3aRFxcnPVvg8GAXq/H09OTyMjI\nJtVEDAYDGRkZREdHN7sPIC0tjejo6A61GxQURERERJOfIUO6Pxe7MeEDWl5BnFtUTX6JeeWrPT+E\nnNevirziakD82kL8lNET/OzZCRA/ZYifMjr0/o22YyfASfwuHil2dL+cwioKbawsAk4QZOfn5/Pc\nc8+Rk5ODVqvlhRdeIDIykpiYGObMmcOpU6fYvn07Op2OtWvXEhYWxqhRo5g+fTo6nY5169ZRV1fH\nli1bKCwsZMaMGfb+l2ymte3BLaNg3p5uDB8SaBc3ED+liJ8yxE8Z4qcM8VNGj/Abaj+/cKfxq3Va\nP1tw+CB72bJlzJgxg1tuuYXp06eTkZHBW2+9hYuLC6Ghobz99tu8+eabXHrppeFptBAAACAASURB\nVMTFxfHGG2+g0Wjw8PDgvffe4+uvv2bq1KmsX7+etWvXdjhdxBFpbXtw61bl0SG4utrvJXVav3Pi\n1xHETxnO7ndJlPi1hfgpoyf4udnRL9yZ/LKdx8+Sjz1yWJBNx3X4INvd3Z2VK1eyd+9ejh07xrvv\nvsvAgQOt90+bNo0vv/ySY8eO8cknnxAREWG9LyYmho0bN3Ls2DG++OILxo8fb49/QTVa2h7caDRZ\nV+aOtUPpm8Y4rV+y+HUE8VOGs/vZo7RWY8RPGeKnDEf3a7w9uKP7pTmhX2y4bVVPHD7IFppi2X47\nJasUMK8krqg2b7Bjz3w1C87ml55TTnmV+HUU8VOGM/vZY5OIixE/ZYifMhzdz5JX7PB+F5zDr3H8\nEhtu20i2Q1QXETrO8KGBHDyTy66jWUQNDsRgMAHQt48nQ/r729nO+fyMRvHrDOKnDGf1C/L3ZKj4\ntYv4KUP8lDF8SCAHTju+3+6jWUQ7gV/j+GVAiK9Nx5Qg28mYd3kEB07nkpxZyvtbT+HrZX4Jxw4P\nRaPR2NnOOfwOns4lSfxsQvyU4ax+48SvQ4ifMsRPGTfMiOSA+NlMa35jo233k3QRJ8PPx4PnH7yc\nybH9AajS6gH7lr5pjDP4/U38bEb8lOG0fnbOl7QgfsoQP2U4vJ+3O3978HKmjBI/W2jdz/bPZwmy\nnRAvTzf+cs9Urp02DDDvVmTPIu4XI37KED9liJ8yWvbrZ2erBsRPGeKnDGfwe3KJ+NlKy362fz5r\nTCaTSS25nk5WVhazZ8/mp59+YvDgwfbWwWQysTc+Gx8vNybF9Le3TjPETxnipwzxU4az+Hl7ullH\nFh0J8VOG+CnDZDLxy4lsvDzEzxYu9rM1/pMguxM4WpAtCIIgCIIgdC22xn+y8LETGAwGAHJzc+1s\nIgiCIAiCIHQHlrjPEgd2FAmyO0FBgXlnojvuuMPOJoIgCIIgCEJ3UlBQwLBhwzr8eAmyO8GYMWOY\nPn06q1evxtXV1d46zcjMzGTJkiV89NFHDBkyxN46LfLcc8/x5JNP2lujReT8KUPOnzLk/ClDzp8y\n5PwpQ86fMhz9/BkMBp5++mnGjBnTqedJkN0JvLy8CA4O7lQvpjupq6sDICwszGFzxn18fBzWTc6f\nMuT8KUPOnzLk/ClDzp8y5PwpwxnOX3BwMF5eXp16jpTw6yS/+tWv7K3g1Mj5U4acP2XI+VOGnD9l\nyPlThpw/Zcj5U4Yt50+C7E5y7bXX2lvBqZHzpww5f8qQ86cMOX/KkPOnDDl/ypDzpwxbzp8E2YIg\nCIIgCIKgMq7PPPPMM/aWENTDy8uLqVOn4u3tbW8Vp0TOnzLk/ClDzp8y5PwpQ86fMuT8KaMnnj/Z\njEYQBEEQBEEQVEbSRQRBEARBEARBZSTIFgRBEARBEASVkSBbEARBEARBEFRGgmxBEARBEARBUBkJ\nsgVBEARBEARBZSTIFgRBEARBEASVkSBbEARBEARBEFRGgmxBEARBEARBUBkJsgVBEARBEARBZSTI\nFgRBEARBEASVkSBbEARBEARBEFRGgmxBEARBEARBUBkJsgVBEARBEARBZSTIFgRBEARBEASVkSBb\nEARBEARBEFRGgmxBEARBEARBUBkJsgVBEARBEARBZSTIFgRBEARBEASVkSBbEARBEARBEFRGgmxB\nEARBEARBUBk3ews4E1qtllOnThEaGoqrq6u9dQRBEARBEIQuxmAwUFBQwJgxY/Dy8urw8yTI7gSn\nTp3ijjvusLeGIAiCIAiC0M1s2LCByZMnd/jxEmR3gtDQUMB8ksPCwuxsIwiCIAiCIHQ1ubm53HHH\nHdY4sKM4TJB9+PBh/v73v5OamkpQUBD33Xcft99+O2VlZfz5z39m//79+Pv78/DDD3PLLbcAoNPp\neOaZZ/jxxx9xc3Pjzjvv5MEHHwTAZDLx6quvsnnzZgwGAwsWLGDlypWK0jwszw0LC2Pw4MHK/2lB\nEARBEATBKehsDOkQQXZZWRkPPfQQf/nLX5g3bx5nz57lnnvuYejQoWzcuBEfHx/i4uJITExk6dKl\nXHLJJcTExPCPf/yD7OxsfvrpJ4qKirj33nsZOXIks2bNYsOGDezatYsvv/wSjUbDAw88wCeffMKd\nd95p739XcFCOJuQTdzKbJTeMws/Hw946giAIgiA4MQ5RXSQ7O5uZM2dy44034uLiwujRo7n00ks5\nevQoP/74I8uXL8fT05OxY8cyb948Nm/eDMCXX37JAw88gL+/P+Hh4SxevJjPPvsMgK1bt3L33XfT\nr18/QkNDeeCBB6z3CUJL/Hvbab7ff54tO5LsrSIIgiAIgpPjEEF2bGwsL730kvXvsrIyDh8+DICb\nmxtDhgyx3hcREUFSUhJlZWUUFhYSHR3d7D6A1NTUZvclJydjMpk65FRSUkJaWlqTn8zMTEX/p+DY\nVNbUAbD72AWMxo5dJ4IgCIIgCC3hEOkijamoqGDZsmXW0eyPP/64yf1eXl5otVpqamoA8Pb2bnYf\nQE1NTZMyK97e3hiNRnQ6HZ6enu16rF+/njfffFONf0lwEmp1egAKS2s4nVbEJVEhdjYSBEEQBMFZ\ncaggOzMzk2XLljFkyBBee+01UlJSrEGzBa1Wi4+PjzWA1mq1+Pn5NbkPzAF3bW2t9Xk1NTW4ubl1\nKMAGWLx4MfPmzWtyW25uLkuWLLH13xMcnFqdwfr7riNZEmQLgiAIgmAzDpEuAnD69GluvfVWZsyY\nwdtvv42XlxfDhg1Dr9eTnZ1tfVxaWhrR0dEEBgYSHBxMWlpak/uioqIAiIqKanZfZGRkh32CgoKI\niIho8tM4bUXoWRiNJnR6o/XvX+IvoKsztPEMQRAEQRCE1nGIILuwsJD77ruPe+65h5UrV+LiYtby\n8/Nj9uzZvPLKK9TU1HDixAm2bdvG/PnzAbjxxht54403KC0tJT09nfXr17NgwQLrfR988AG5ubkU\nFhbyzjvvWO8ThIupvSigrtLqOXw2z042giAIgiA4Ow6RLrJlyxaKi4tZu3Yta9eutd5+11138eyz\nz/L0008zc+ZMfHx8eOKJJxg3bhwAjz76KH/729+YO3cuGo2Gu+66i7lz5wKwaNEiCgsLWbhwIXV1\ndcyfP5977rnHLv+f4Pg0ThUJ9POktLKWXUezuGzsQDtaCYIgCILgrGhMHS23IZCVlcXs2bP56aef\nZDOaHkZuURVL//YjAAtnDWfLjiTcXF34+Jlr8Zea2YIgCILQa7E1/nOIdBFBsDeN00WumjQYDzcX\n9AYjv8Rnt/EsQRAEQRCElpEgWxBomi7St48XU0eHAfDz8Qv2UhIEQRAEwYmRIFsQaBpke3m4cumY\nAQAkpBdTp5cqI4IgCIIgdA4JsgWBhnQRFw24ubowJjIYAJ3eyLmMUnuqCYIgCILghEiQLQg0jGR7\nerii0WgICfQmLNi8sdGp1EJ7qgmCIAiC4IRIkC0IgLZ+S3VPj4aqlmMizTs+nkopsouTIAiCIAjO\niwTZgkBDuoinu6v1ttH1KSMJ6cXoDcYWnycIgiAIgtASEmQLAqCtNQfZXh4NQfaYKHOQrdUZSMmS\nvGxBEARBEDqOBNmCQKOR7EZBdv++PoQEeAFwOlVSRgRBEARB6DgSZAsCUGvJyXZvyMnWaDSMiTLn\nZZ+UvGxBEARBEDqBW/sPaZ1NmzZ1+LG33XabkqYEoUtpXF2kMaMjg9l1NIuzaUUYjCZcXTT20BME\nQRAEwclQFGS/8847HXqcRqORIFtwaFpKF4GGvOwqrZ707DKiBgd2u5sgCIIgCM6HoiB7x44dankI\ngl3R6ppXFwEYFOpHoL8npRW1nEotkiBbEARBEIQOoSjIvpi8vDxSU1MxGMwBi8lkQqfTcfr0aZYv\nX65mU0ILVFTryCmsYsTQIHurOB2WdBGvi0ayNRoNoyOD+SU+m9OpRSy4MsoeeoIgCIIgOBmqBdkb\nNmzgueeew2g0otFoMJlMgDlIGTdunATZ3cA/Pj3KoTN5PLZoIldPGmJvHaeitq75ZjQWLqkPsk+l\nFGE0mnCRvGxBEARBENpBteoiH3zwAQ899BAnT54kODiYXbt2sW3bNmJiYpgzZ45azQhtcDatGICv\n96bZ2cT50LYykg0wqn5TmopqHRcKKrvVSxAEQRAE50S1IDs/P58FCxbg7u5ObGwsx48fJzo6mpUr\nV7J582a1mhFaoVpbR2VNHQCJGSVk5lXY2ci5qG0lJxtgaFgfa/CdlFnSrV6CIAiCIDgnqgXZgYGB\nVFSYA7uIiAgSExMBGDRoELm5uWo1I7RCQWlNk793HM5U9fgmk4ncoipyCquoqdWremxHoLUSfgCu\nLhqih5gXPCZlyM6PgiAIgiC0j2o52VdffTVPPfUUa9asYdq0aaxZs4YrrriC7du3M2DAALWaEVqh\noKR5kL14bqxqdZ1/Pn6Bl9Yfsf7t6eHK1FFhPLF4EhqN8+coW3KyW0oXARgxJIhTKUWck5FsQRAE\nQRA6gGoj2StWrCAmJoaEhARmzZrF1KlTWbx4MZs3b2bFihVqNSO0QkFJNQAe9ekOxeVa4s8VqHb8\nnw41HRmv1Rn4+fgF8oqrVWvDnjSki7Tc77RUbEm9UE6d3tBtXoIgCIIgOCeqjWT7+vqyZs0a698v\nvPACq1atwtPTEzc3VSsFCi1gSReJGhSA0WQi8XwJPx3KYGJMP8XH1tbqOZlSCMA980YzNMyf1e/v\nB6C8SkdYsK/iNuyNtU62Z8sj2cPr00X0BiNp2eVSJlEQBEEQhDZRLfr94osv2rz/pptuUqspoQXy\ni81BdmiQN2OiQkg8X8K+UzlU1tTh5+2u6NjxSQXU6Y1oNDB7yhD8fTxw0YDRZA6ynR29wYjBaC45\n2dLCRzCf10A/T0ora0nKKJEgWxAEp0ar0/PxN2fx93bnt9fG2FtHEHokqgXZL7/8cpO/9Xo95eXl\neHh4EBMTI0F2F1NQak7bCA305orxg3jvi5PU6Y38fPwCc6eHKzr2obN5gDllIsDPEwB/Xw/KKnWU\nV9UqOrYjYEkVgZYXPoK53vvwoYEcOpPHucxSbuguOUEQBJXR1up59sMDnEg2z1DOuXQYIYHedrYS\nhJ6HakH23r17m91WVlbGqlWrmDhxolrNCK1gSRcJDfLBz9udaWMG8PPxC+w+mqUoyDaZTBypD7Kn\nxPa33t6nPsguq3T+kWytrqFailcLm9FYGDE0iENn8uxWxu9MWhGHz+Yxf0YkQX287OIgCIJzU62t\n468fHOB0apH1tuJyrQTZgtAFqLbwsSUCAgJ49NFHef/997uymV6PwWCkqEwLQL8g8wfl9EvMFV0S\nzxejVVByLz2nnML6Y09uEmSbR7R7QrpIbV2jkexW0kWgIS87K7+Sqvqa5N2FyWTiH58eZfNPSTz6\nj10kpBd3a/uCIDg/1do6nnlvf5MAG6C0wvlnJAXBEenSIBvg/Pnz1NTUtP9AwWaKyrUY63OKQ4N8\nABgbHQKA3mDiTJrtAdnh+lHsvn28iBwUYL29j68H0EOC7A6kiwAMH2LOwzaZIDmre+tl5xVXk1tk\nTgkqLq9l5dt7+SYuDZPJ1K0eQs8mPaecP7/9CzsOZ9hbRVCZypo6Vr0Tx9n6DvqyX4+lb/2MWEmF\n1p5qQhdQU6vnq59T2XU0y94qvRrV0kUef/zxZrdVVFQQFxfHzTffrFYzQgs0rpFtGckO8PMkfEAf\n0nPKiU8qsLnKyKEz5iB7cmz/JvWwLUF2WaXzj4A0DrJbq5MN5v95QLAvOUVVJGWWMm54aHfoAebF\npwDenq4E+nuRU1jF2s9PUFap47e/GtltHo3R1ur58VAGfXw9uHLCYLs4COqhqzPw4rpDZOZVcjq1\nkEA/L1WqEwn2p6Jax6p34kjJKkOjgYcXjuPaaeFsP3Ce4nKtjGQ7MbuOZrHzcCYx4X2ZMqo/w8L6\nsH1/Oht/PEdpRS0aDUwYEWpdTyV0L6oF2R4eHs1uCwsL4+mnn2bBggVqNSO0gKVGtq+XGz5eDZVE\nxg0PJT2nnBPJttXLLq/SkXjePOrROFUEevBIdhvpIgDDhwaSU1TFuYzuzcs+kWReoDQmKoTHFk3i\nlQ1HOHw2j//tSubmmVF4eXZfmcw6vbHZh3hseDChQZLT6cx88n0CmXmVgLly0IvrDvHy769kcD//\nLmmvTm+kqKymR5QAdWTKKmtZ9U4cadnlaDSw/NYJXDN1KACB/ubAS4Js50Sr0/P2lnhqavUcTczn\nk+8TcHdzoU5vtD7GZDIPxEmQbR9U+2Z+/vnn1TqU0EkaL3pszLjhIWzdk0LKhTIqqnX4+zTvCGXm\nVfD+1lP86tJhXD5uYJP7jibmYzSBm6sL40c0HbW1vGF7QpBtWfjo5uqCq2vbGVTDhwSx59gFkrox\nyDaZTNYqAGOjQ/Hzdmf5reNZ8ux2amr1xJ3MYdbkIV3uYTSa2HMsi/XfJTTZhMhkgrPpRYQGyWi2\ns5KQXsz/diUDMGvyEA6fzaO8SseaDw/w8u9nKi4DejHV2jqe/FccyZmlPLF4ksyEdBG5RVWsfn8/\nWfmVuGjg0d9O5OpJDZ8VgfWf4yU9YEayN7L/ZA41tXpcXDT07eNFYWmNtdzu1ZOGsOtoFkajiWJJ\nB7IbioLsTZs2dfixt912m5KmhDbIL2mokd2Y0ZHBuLhoMBpNnEwu5LKxTYNog8HIy+uPkJpdxtn0\nYkZHBltHNgAOnckFYExUMN4XjZQ2jGQ7/4ezZeFjW/nYFkYMNS9+LCzTUlRWQ3BA14/eZuRWUFr/\nJThuuDnXPqiPF5Nj+nPwTC4/Hcro0iDbZDJx6Gwe6745S3pOOQAaDVw1cTDpOeWkZZdzNr1YAiUn\npbbOwGsbj2I0wZD+fjy8cBznMkr4y7/iuFBQxcvrD/P0fdOapIspQVdnYM2HB0nONK9r+ODLU0wZ\nFdbsM0ZQxtm0Ytb8+wDlVTpcXTQ8tmhis/dokIxkOzU/HjKvnZgU049V915Kek455zJKiQkPYlhY\nH46fK6C4XEtJuby+9kLRp9o777zT5O+cnBw8PDwYMmQIbm5unD9/Hp1OR2xsrATZXYglXST0ohJM\nPl7ujBgSSML5Ek60EGR/tTeV1OwywLxIYuMPiSz79VjAvLBvb3w2AFNHhTVrM6C+ukhlTR0Gg7Hd\nEWBHxrrbYzupIgCRgwKsHZekzNJuCbIt+dh9fD0YFtbHevs1U4dw8EwuJ5ILyS2q6pJpd12dgZc3\nHGHfyRzrbZeODuPOubEMG9CHf391mrTschLO26esYU9CV2cgp7CKoWH+qgW0HeGT7xK4UFCFi4uG\nR2+fiIe7K2OiQnjwN2N5c3M8RxLySblQRvTgQMVtGQxGXt5wxLqDrIuLhuLyWjb/dI67rh+l+PiC\nmT3Hsnht4zHq9EZ8vd1ZefeUFteQSLqIfTCZTIrf4/nF1dYZztlThqLRaIgYGEDEwIYCBUF9POtz\n7mUk214oCrJ37Nhh/f2dd97h+PHjPP/88wQGmj+MKysrefLJJxk4cGBrhxBUoLV0ETDnZSecL7EG\nahbyS6rZ8F0CYK4cUlyu5bt96cy/IpJ+QT68vvEYRqOJASG+zLl0aLPjWkayTSZzoO3M+V6WnOy2\nFj1a8PJwI2JgH1KyyjiZUsi0MQO6Wq9RqkgILi4NH8yTY8Po4+tBeZWOHYczWaTyrm1anZ7n/n2Q\n4+fM187oyGCW3DCKmPC+1sdYfk+9UIa2Vt+tueE9CYPRxJ/f/oXEjBLmTB3KwwvHdUvHtVpbx9dx\naQD8+qroJjuZ/urSYWzcnkhhmZYjCXmKg2yTycTa/56wdtgWz42hRqvn853JfLE7hWunhdO/b/PP\nMHtgMpnY8H0CB07l8qc7JzOkf9fkpXcFJ5MLeXnDEUwmCAv24anfTWvVP9DfXF2kVNJFuhy9wUjc\niWy++jmV87nlXH9ZBAtnj2gxFataW8f+U7nkFlVxw+URLX6//nQ4E5MJ/H08WhwIAwjy9wLKKC6X\nINteqPYp/sEHH/DHP/7RGmAD+Pn5sXz5cj777DO1mhEuwmQyWUey+7Ww8GxsfXpBVn4lRWU11ue8\n89+TaHUG+vh68MrvryQk0BuD0cR/vj7D5p/OkZ5jXiTz+9smtLhBiyXIBuevMNKZdBGAiSPNFReO\nnM3vMicLBoPROuo39qKRKHc3F66aZJ7+/elQhrWMoxpY6ulaAuzF18Xw/EOXNwmwAWLCzUGZ0Wgi\nqZvLGvYktu9PJ7E+z/+Hgxk8/59DTeq3dxV7jl2gVmfAzdWFm6+KbnKfRqNhUv2CZzWu9WPnCvh+\n/3kA5l8Rya2zR3DrNSMI9PekTm/k39tOK25DLTb9eI5NP5g/Bx3Jqz2qaup49dOjmEwwLMyfl5df\n2WYHIag+eKuqqUOn8vWWXVjJrqNZqh/XGfl+/3nue+4HXlp/hITzJdTUGvh8ZzL3/+0H/rcrmfhz\nBcSdyGb7gfP8/eND3Pn0d/zj06N8uj2RR1/dxdmLyvAajSZ+qk8VuWrSYNzdWg7lLOlAJTJTYTdU\nC7I9PT1JS0trdvvp06fx93eeUQBno6qmjppa84dYaGDzUaCYYX3xqH8DWkZE98Znc7A+3/p3N44m\nJNCbO+eaR0H3ncxh04/nAJg3I5LRkcEtttvHryHIdvbFj5aFjx1JFwGYFGMOPC4UVJJbVNVlXgAp\nF8qo1pr9LPnYjblminmWIb+kxhqMK6VOb+Spd/ZZN6y4b8EYbpszssXpzSB/LwbUp6nIBjm2UVGt\nY923ZwGsu+4dOJ3LU+/EUVndte+tHw6ag97plwxo0nG2YKkqlHi+WNH73GQysXF7IgAjhwVx341j\n0Gg0+Hi5c+fcWAB+ic/mlErXsBK+3ZduneUDcxnTFCfpQL77xUkKS2twc3XhicWT251hbLwGR63R\nbKPRxJd7UnjkpZ28suEIj7++x7qWozeSnFXKm5uPWzeMmxzbn5tmRuHh7kpFdR0ffnWav7wTx/P/\nOcQbnx1nb3w2Or0RVxcN7m4uFJZpWfn2Xv67M9m6L8LptCLr4nPLd0BLWHYGlnQg+6Ha3O5dd93F\nypUrOX36NDExMZhMJuLj49m4cSP/93//p1YzwkXkN66R3bf5SLaHuyuxEX2JTyrk5+MXOJqQby1O\nPzY6xLrS/KqJQ9i625yjbTSaCAv24a76L7+W8PJww8PdFV2dwemD7IZ0kY69HWKGBeHr5UaVVs+R\ns3ncMCOyy9wsaT4hgd7WYLYxEQMDiBocQEpWGT8eylCldveeY1nWUdWHF47juunhbT5+ZHgQOUVV\nJKR3XV62Vqdn55EsJo3sRz8HSSmwFaPR1CTt55PvEqiorsPLw5WXl1/BziNZ/OfrM5xJK+bJf8Xx\n3LLL8GuhMpBS0rLLOJdhDh5/1UJKGJjTzdxcNegNJo4l5jNzom2LW08kF1o3QVn0q5gm///sKUP5\n+pc0Ui+UsemHc4yJat6Z7C7iTmTzr8/jAbgkKoTi8houFFSx6cdz/HnJ1E4fr7Jah4uLpklp1a7i\nl/hsdhzOBODuG8xrJtqjSZBdUUu/FlIOO0NhaQ2vbzzG8Ubpiek55Tz22m6W3DCKeTMim7z23YnJ\nZOKHgxnkFVfTt48XwQFeDOnvz6BQvy5t15IeFRLozZpll1nbu2lmFJ9uT+THgxkYjCY83F3x8XJj\nUKgfMycM4vJxgygu1/LCfw5xoaCSf287zfYD57ls7ABrpyVyYECTTeIupm/962uvdBGTycS6b88S\ndyKbe+aN5tJuSK9s3LbRBK52ut4sqBZkL126lMDAQDZt2sRHH30EwMiRI3nuuee4/vrr1WpGuAhL\nqoiri8aaX3cx44aHEp9UaN1YBsxVBP7freOto5MuLhrunT+av7wTB5hrqbaXX9vH14PC0hrKekiQ\n3dF0EVdXF8aP6McvJ7I5nJDfpUG2pT72uOEhrS6UuWbKUFKyTvLzsQv4eblz89XRNn9Zmkwmvtid\nAsBlYwe0G2ADxIb3ZdeRLM6mF6uyoKclp398epS4EzmEBfvwz8evdqpKFHV6I/FJBcQnFXAiuZD0\n7DKGDwnixisjGRTqxzf1OdG3zB5BcIA3C2cNJ9DPg39+dpzUC2U8895+/vrAdNUDtR8Omqeb+/X1\nYWx0y50zb083xkSGcDypgMMJeTYH2Rt/MI9ijxgayISRTdtyddFw88woXvnkKGfSiqjTG3B369h7\nUU3yS6p5ZcMRjCZz8PKXe6ey72QOr208xr6TOZzPLW+y8Lg9zqYV88z7+/DycGPt/83q0kA7p7CK\nt7YcB8yDJzdeEdWh5/n7eFgXcisdyS6rrOUPr+22jprOnDCYKycOYu3nJygsreG9rafIKqjkod+M\nU9SOrXz5cyrvbz3V7Pbb54xk0bUtz9SpwYFT5iD7srEDmgT0wQHePHLLeB64+RI0Gg1uLazB6OPr\nwauPXslbW+LZc+wCFwoq2fxTkvX+2VParioVaN3Rs1b1z+as/ArOZZQyc8KgVteP/HAww+r7t48O\ncv9Nl3TZ96XJZOLw2TyOnSsg9UIZ6dllGE0m/vbQDFUWbduKqt9Ut9xyC7fccouah1TMmTNneOqp\np0hOTmbYsGGsXr2a8ePH21tLNSyLHoMDvVvtsY0fEcrH35inowP9PFl07Uh+demwZm+McSNC+cs9\nU3F3c+WS6PZHkwL8zEG2s5fx62xONphLJv1yIpsTyYXo6gx4dDDVpLNeZ9LMKRutBUFgLqX3xe4U\n8oqr2fZLGt/tT2f2lKHct2BMh0fnLcQnFVhHSW6eGd3Oo83E1udpV1TryC6s6vTIUHsf/t/tSyfu\nhPmLKreomg+/Os3DC+3zRd1ZLFtZW8rVWUjMKOGl9Udw0Zg3fgkL9uGmmQ2B0TVThwEaXt90jMSM\nEla/v5/VS6ertrBUV2dg1xHzqOecqUPbHF2cFNuf40kFHE3Ix2A0dXpkYAyy0gAAIABJREFU6GRK\nIadSzNfx7a2kHVk+b3R6I+cySltNU+tKvvklDZ3eSB9fD55ZOg0fL3dmThzMp9sTySuu5rMfz/HE\n4skdOlZKVimr399HtVZPtVbPwTN5XGVjB6Ut9AYjX+xO4dPtiejqDPh4ufH72yd0eLTYxUVDgK8H\nJRW1ilMKvtidQmlFLZ4eriy/dby1XOCo8L68uSWeX+Kz+TYunZkTBnf76xt/roAPvzLn1vfr6wMm\nE8XlWvQGExt/MJ+7JfNGqR5oZxdWcj63AoBpo1sexW2vQ+nj5c4f75jEvMsjiTuZTdyJbPJLavD3\ncW+309u3fuBNV2egWqvHV4V690ajia17Uvj4m7PoDUYqqnUsuLJ5py4lq5R//fcEYN6DQm8w8q//\nnSSvpIbowQEcOptH/LkCBob68ewDl7WaV94RqrV1rP38RLMt5F005spp9kTRJ/arr77Kgw8+iLe3\nN6+++mqbj33ssceUNGUTtbW1LFu2jGXLlnHLLbewdetWHnnkEXbs2NHiDpXOiCVdpKVFjxaiBwdy\nz7zRGE0mrr8svM0Rlc5M5/Spn8Iur+whI9mdCJQt203r6gycSinqku2nN25PRFe/sUBL+dgW/Hw8\n+OfjV/H1L2l8sTuF8iod3+8/j7ubCw/cPLZTbVpGsUcOC2q2yLE1hob1wdvTlZpaAwnpxZ0Ksg+f\nzeP1jccYGx3CA78e2ywvOC27jPfqR58aV8G5dHRYs11IDUYT3+1L54eD55l3eaR1Vzt7YV48us8a\nYEcNDmBsdCjhA/qwN/4Ch87kYVmret+NY5p11K6ZOpQ6g5G3t8RzJq2YZz88wJ+XTLXpi7JOb2T3\n0SwGhPgSG96X/adyqKiuw0UDsye3fZ4mx/bjgy/Nay+SMkuIGdb2daE3GMkvqSbA1xMfLzdrLnb0\n4IBmr5mF4ABzOlROURWnUgq7PQirrTOw/YA5P/3aacOsuaxuri4snDWct7bEs/f4BRZdG9Pu9Z2Z\nV8FT7+6jStvw5b73+AVVg+yqmjqOJuaz6YdEaxAX6OfJH347sdOzWIH+noqD7MpqHV//Yp6RufGK\nyCb1uP18PHjijklkF1SSll3O2s/jee2xq1ocue0Kcouq+Pu6wxiNJob09+fl5Vfg4+VOnd7AS+vN\n5Un/uysZnd7A0gWXqJrOcuCUee2Tv487oyI69nnaEhqNhtiIvsRG9OXe+aPJyKvAz9u93Zz7oD4N\n95dUaBUH2fkl1bz26bEm63++jUvjxisim3RQKmvqeOHjQ9TpjfQL8ub5h2bw1pZ4jibmWze9avCq\nJT6poNXPBjCv9ykorSFqUABhwb5NXqPkzFJeXH+YnELz+qjowQGMiggmYmAfYsL7dtmOtR1FUZB9\n7Ngx6urq8Pb25tixY60+rjtrvjZm//79uLi4sGjRIgAWLlzIf/7zH3bu3Mm1115rFyelZOZVsPtY\nFtPHDCBqcGCrNbIbo9Fo+PXVHRuV7Ax9fLtn18euSEFojHXhYydGsoMDvIkcGEBqdhlHEvJUD7JP\npxbx+U7zNNuCK6Parcft4+XOLbNHMH9GJP/edppv4tL5Ni6d+TMiGdjBoDcjt5wjCeYqEo1HVdvD\n1UXDiKFBxCeZ825nt7EQpzFllbW8tvEoZZU69hy/wKnUQpbfNsG6sFRbq+el9Yep0xsJDfLmH4/O\n5NkPD5B4voQ3PjvGG3+cZQ3KU7JKefvzeGuO8btfnOSysQO6JRe2JbQ6vdUVYPmt45lz6TDr/bMm\nD+FCQSU/HDhP3z5eTB3dcgmuudPDqasz8N7WU5xILuSJN37mqd9d2uma6B9+dYpte81BUGhQw6zX\nxJj+zTaxuphBoX7WAPjw2bx2g+znPzpkXVjt5eFqrUPf2ii2hTFRweQUVXEypZDb5oxs938yGE2s\n++YMXp5u3HbNCEWfEXuOZpk7HS4arr8sosl9s6cMYdMP5lKGm386x6O3T2z1OLlFVax6J47yKh2+\n3u5cM2UoW/ekcDQxn2ptneLrcdeRTH44mMHp1CIMjaoJXTttGEtuGGVT7n6QvxdplCtKF/lqbxo1\ntXo8PVxbHNV0dXXhod+M44k3fuZ8bgVf/ZzarJqNWhSV1ZCUWYpWZ0BXZ+Crn1OpqDa/Hn+5d6r1\nNXB3c+VPd07mH58cZc/xC2zbm4abqwu/u3FMk+MlZZZwNCGf66aHd7pU7YHT5vfBlFFhqpXk1Gg0\nHU5bapxzX1JRqyjgPHAqh398etTaeZwU048jCflcKKjiVGoRl9SvpTCZTLz26VFyi6pxc3Vhxd1T\n6NfXh1W/u5S3t8Tzw8EM3Fw1jI4MJq+4mtyiauJOZLcaZF8oqGTFW3ut17u3pxsDQnypqdVTWV1H\nZY0OkwncXDXcfcMobrwiym55/y2hKMhet25di787CmlpaURFNX3DR0REkJSU1G6QXVJSQmlp0yne\n3Nxc1R07Sl5xNZ9uT2Dn4UyMJti6O4Vnlk6noKT1GtldTYCfZddH9YNsk8lEQnoJW39O4cCpXOZf\nEcm980er3g40pIt0NrViUmw/a5C9lEtsbj+3qIrE8yVMju2Pr7c71do6/tGoDNedbSxAvRgvTzfu\nmT+aA6dzKSrT8tHXZzq8YGvrnlTAPJ06vZMLVGLCzYtrO1NhZO1/T1BWqcPTwxWTCYrLa3nmvf2M\nHBaEtlZPcXktFfULx/54xyQC/Dx57LcTWf7qLorLa/nz23sJ8POksqauPv/OfFzLFOH2Axmd6iyo\nRX5xNa9vOmZNkVh28yVNAmwLg0L9WDKv/Wv6xiujcHd35V//PUFmXgWPv76HPy+Z2uHR3qz8Cr6N\nS7f+XdBosXRrCx4bYy7l149te9M4cjaPxde1fj2eTSu2BtjQsNFT5KCAVjsSFsZEBfPDwQzOppdQ\npze2O328/2QOn+80j4pNie1PlI15lyaTydoBmT5mgLXCiwV3N1duvjqa9744xe6jWdw5N7bFTm9R\nWQ2r3omjqEyLl4crzyydxuBQP77+JZU6vZGDp3O5qn6hucFg5OCZXCIGBnS4w7TraBavfHLU+reL\ni4YxkcEsvi6WWAWjpJZArMTGxXHV2jq+3GOeAZvbRiAaE96XOVOH8sPBDD7dnsAV4wcR6O9JWnYZ\nBSU19Ovrw8AQX7w83DidWsTOI5nEncxhQIgvf394RpspeZU1dfwSn82eY1mcTCnEdFE1UxcNPLF4\nEgNDmg44uLm68Ngdk3Bzc2HH4Uy+2J3ClFH9rel52YWVPLn2F2pqDew4nMnq+6d3+PUqq6zlbH26\n37QxbV/7XYWXhxs+Xm5Ua/U2v74Gg5F13561vtf8fdx5eOF4Lh83kN+/uovUC2V8v++8NcjefTTL\n2rlYetMYhg8xl3l1c3Xh/906npuviiY4wAsfL3c+35HER1+fYf+pXB5e2PKmdruPZjXpUNbU6km9\nUNbkMWHBPjyxeHKTOv+Ogqo52YmJiYSFhREQEMDu3bv5/vvvGTNmjHUkubuprq7G27vph6GXlxda\nbfsX2/r163nzzTe7Sq3D6OoMfPJ9Alv3pKA3NFxoWp2BZ97bh2Xwpq10ka7CMopYpnJOduL5Yt79\n4qR1VBLgf7uSuSQqmCmtFN1XQmcXPlqYFNOfzT8lcaGgipzCKgaEdG500WA0sXV3Mhu+S0CnN+Lt\n6cqsyUMpr9KRV1yNm6uGxxZN6nS+t5eHG3fOjbUu2DqdWtRuQFZaUcvO+hzdG6+I7PSoiyUvOyOv\ngvhzBcQnF3Auo4TowYHMvyKyWVDy8/EL/FK/o+jvbhzD2OgQXtlwhKTMUuvor4XF18UwKsLsPzDU\nj3vmjeZf/z1RP01eYX3cgBBfHvrNWPYcu8APBzP4am8q86+ItHl1eVllLWfSikjPLufycQMZ2s7o\nUbW2ji07kvhidwp1eiMA984frcpCn7nTwxkQ7MMLHx+mvErHirf20sfXgyB/T/r28eLKCYOYNbnl\n3OqPtp3BYDQREuDFyiXmxXz7TpoXkXb0/TQ5tj/b9qaRnGXe2KJvn5YXWX/2k7n8Z/iAPjz0m3EU\nltVQUa1j6qiwdkeax0TW52XXGUjOLG03cPxqb6r190Nn8zocZBeV1XA+p4Jxw0NwdXXhTFqxdefb\neTMiWnzOr6YO49PvE6msqePrX9Ka7U5ZVlnLqnfiyC2qxt3Nhb/ce6l1xH/c8FCOJOSzNz7bGmS/\nv/UU235Jw83VPHJ+6zUj2hwlzS+uZm191ZMRQwO56cpoJsT0a3Ejk84SWN+urSPZ38alU1lTh7ub\nS7ud2rtvGGVNVXrin3uoqKmzfv5aaDz7AeZ0gONJBa1uuFJSruWx13ZTWNbwve7mqsHb062+Yoc7\nC2dFW2fILsbVRcMjt4wnPbuc1OwyXt90nDcevwp3N1deWn/EWiI3u7CKJ/75M0/fN43oIe1fa4fO\n5GI0gYebCxNGqJ9O2FGC/D3NQbYN6UAV1Tqe/+iQNT1k5NAg/nTXZGtK0nXThvH25yf45UQ291dd\ngpurxpr7Pjm2P3MvWjiv0Wia1G2fPnYAH319hopqHafTipqtPTKZTPx8/AIAN1wewdzp4aRcKCW/\npAZfL3f8fNzp4+vB6MjgTg+SdReqWW3atInVq1fz0Ucf4efnx8MPP8y0adN4++23yc/P59FHH1Wr\nqQ7j7e3dLKDWarX4+LQ/6rt48WLmzZvX5Lbc3FyWLFmipmKbpGSV8uqnR8mw5Nz5e3Lr7BGMHR7C\n0+/us9bdhJZrZHc1ffzUTxfR6vSs+fCg9QM/vL4MVXpOOW98dpw3n5jVYj1fZW12PicbLirll5DH\nvE4EU5l5Fby+8Zi1VB5ATa3BmtcIsOjamDbLM7XFVZOG8OUec0nGD786xcvLr2wzyPkmLo06vREf\nLzfm2JDLPLJ+BMFkwlqhBiA+qZCte1KYOXEwV04YTICvB66uLqz93LwgZvzwUK6bNgyNRsOL/+8K\nc4mtoir6+HrQx9eDIf39GXlResL1l4VTq9NzoaAKP293fL3dCQn0Ysa4QXi4uxLk78UPBzPIL65m\n/6kcLh/bfMdZg8FIXnE1WfmVZOVXkJVfSUW1Dr3BRJ3eQHG5lsy8Suvjtx/M4K0nrm51uv9cRgnP\nfnDAet0G+Hmw5IbRquaFjx/Rj5eXX8FfPzhATmEV5VU6yqt0nM+tsG708uBvxjW5Zk4mF1pHle68\nfhQjhgYxYmgQd9/QuS3Mx0SFWEt2LnvhJ8YND2FiTH9mThhkPSfJWaUcPmuuYHTrNSM6Pbrar68P\n/YK8yS+p4VRqYZvPT71QZq3jDnDwdC63t5NiUlKuZfOOJL6NS0dvMDIszJ+lCy7hu/3pgPmzprXO\nqJenG9dND2dL/fNvnT3Cugi1qqaOp9/bR2ZeJa4uGlbc1XQL8xnjBnIkId+aMnI+p8K6y6beYOLL\nn1P58VAGN10ZxbXTw5t1YAxGE69+epRqrR5/Hw/+vGRqu+ljnUHJ1uq1dQbrOo5rpg5t1yvAz5O7\nbxjFm5vjmwTFri4a62ildfZjYACV2jrz+/hkTqtB9mc/naOwTIubq4apo8OYOWEwk2P7d2pwwt3N\nhUd/O4HHXttNfnE1H20zpyFZ1lP89lcj2bonhdLKWla+vZdVv7u0zcXoAPvr87HHj+hn151wA/29\nuFBQZdNI9vtbT1kD7HmXR3DvjWOazDDNnDiYD746Ta3OwM4jmRSW1lBSUVu/HuiSdjvWA0P8CB/Q\nh/SccuJO5DQ7p+dzzZ/NYK5WM2xAnw6VpnQkVHvlP/zwQ55//nmmTp3KmjVriImJ4f333+fgwYM8\n/vjjdgmyIyMjWb9+fZPb0tLSmgXPLREUFERQUNOpB3f37svv/N+u/9/encdFVe//A3/NDMsw7IsI\nyCqIrIoim5CKmlQoLtcFS83rVl6v5lpZt9LrF7OfV+8tLSvrtplllml5K/el3FGREBQlkF0YloFh\nZ+b8/jjMEQIRmHNmWN7Px6PHI4EZznkzy/t85v15v+/is/+xK1BiETB1jBfiHx/MPVk3L43EK7vO\ncYn2o+oqhaBJdvlMsn+5kIVyZR0kYhFeXxiOYYP7oaCkCiu2nUZZZR0++D65wzv8O+pBuUjnkmyJ\nRIygwfY4dyMfF1MKOpxkl1fWYd2OX1FV0wCA7e4wY5w3fruRh5/OZUKuqEWApy2mRQ/q3Ik0P7Zm\nLRnTs8vxW1I+Hhs2oM2frWtQcW3kJoS5dalu1ExmhEEuVrjT9KbkYCuDr7sNrqTeh7KmASeu5ODE\nlZwWtzExNmjRRtJAIm618tEWdo/Bw2Pj5miBIO9+SEovxqEzGVySfTGlACcTc5BbpESBXNnik6GH\n0awMy8tr8Nn/UrG0jfZjyup6vPnZFZQr67jVvOljBwlSD+5sb44da6OR+kcJyiprUV5Zh9TMUly6\nWYhb98qw6t+nMT7UDY+HucLbxRof/8huGvVyttRq452xoQRPjXTHwTMZqKlrxMWUQlxMKcShM3ex\ncclI9LeRYX/TKvaAfmYY2caFTUcEeNrhZGIOUjJKMGPcw3/ucNMqtqZrwZ2ccpRV1HIbFptTqdT4\n6uhtHDyb0WLV9F5hZYsLwolRA9tNCiZGeeDgmbtNj+dsxEYNRG1dIzZ+dBEZuQqIRMDqp4e3KosJ\nD3DEu9/eQEOjGudu5OP7MxlcKdhjQQPw3am7qK5txN6jt7HveDpC/R0wPtQVHo6WsLWU4sCpO9wF\nxfKZQ3lNsAHtkuy9v9ziXq//0sHXq8dD3VBeWYeSilr4uNnAz8MG9tYylFXWIq9YieKyGng5W8HN\n0YIrJ7h0s7DNzjZFZdX45QK7YXX2BB/MHO/d6XPQ8HCyRPyEwdjz8y38fCGL+/qU0Z54OsYHEYGO\n2LD7IkorarHjmyR88PL4h9b+1tY34nrTtFx9lYpo2DRr49cZd3LKmvVd98P0sa3/vjKpIUYFDcCx\ny9n4/vRd7nf8JXpQh8tqRg5xQlZBBS78no8lU1puPNWsYttZmWCwW/crBekI3pLsgoIChIaytZ+n\nT5/G1KlTAQBOTk5QKpXt3VQwERERqK+vxxdffIH4+HgcOnQIcrkcUVFRejmejkrPLuM+cnGwlWHV\n7OHcx+UaTv3MsPlvkUj45DLMZUYd3tzGJ02SXVevQm19Y6c/rimQV8HeRsa9cNbWN3J1X+NDXbnN\nhE52Zpgf64cPvv8dZ6/nISLQEVFD204Yu6KuCxsfNaKGOuHcjXzcuCNHZr4CHk6PXnm+mFKAqpoG\nGBlK8Mr8EO5jzBnjvDFtjBcy8hRwd7TQuon+UO9+GOHbH4lp9/HVsVuICnJqM4k4fTUXCiVb+zzp\nsa6XNrw4dwRu3JHDx90arv3NIRKJUFvXiONXsvHjr38gX95yOuaSKQGCDZaZPMoTSenFSMsqxbVb\nRTiRmI2z1/Na/ZxYLIKjrSmc7c1gbSGFoYEYBhIxZFIDDHZlO6z8cDYDe365hZ/OZ2HUn9qPMQyD\nd75Jgry8BoYGYvxrxaguf/rQUcaGEgwb/ODj52nRwLVbRfjg+2Tky6tw9NI9HL10DzYWxiitYN/0\nFsQFaL0ZaGFcACY9NhDXmlZlr6QWIq+4Ci/u+BWLJgdwbRZnjBvU5cduwEBbnEzMQVpWCVSqtms0\nFco6rlXX0zGD8fWxdNQ3qJCYdr9V7Xt1bQPe+jwR126zG3pNTQwxdYwnfN1t8Nn/UrmSNDMTQ4we\n3v5riq2lCR4LGoBTV3Nx6OwfGB/mhs2fXuaG7CybHtSiq4aGmcyIKxn58ODvqK1XQSQCls8MwmA3\nGzwR4Y79J+7g+OV7qKpt5Mp5APYiQq1mS48eD3VFRGDXLl7aoykXUdY0dKgWXuOHsxk40NQp4skI\nd/Tv4HNZLBa1ubHV1tKk1QVERCBbTlBRVY+0zJJWg4r2HUtHo4ptu6jNa5fG9OhBuPh7Ae7msuVD\nXs6WXGmQh5MlXlsYhlX/PoPCkmpcTy9qswRFWdOAb0+wj0mRCIKUOHaGdRdq7hmGwcc/sDmIs71Z\nu2VAMeFuOHY5m1vws7eRYfq4ji8QjRziiL1HbqG0og7p2WVcV6vmpSJRQ5261WbGzuAtyXZxccHZ\ns2dhb2+P3NxcjB07FgDw3XffYeBA4YZ1tMfIyAi7d+/Ghg0bsH37dri5uWHXrl0dKhfRJ80biKOt\nKd5eM+ahgzec7Mywc2203rq3WJq2HK3emST7q6O3sffILQxyscI/nxsJMxNDHLl4D+WV7KrIjHEt\nVySeGumBC78XIPmuHO/su46i0mpMemwgL0MrHtRkd/7pEBHgCAdbGQpLqnHg9F2seTr4kbfRfHwf\n7GPf6kVaIhHzunlj9oTBSEy7j5z7SiTflbeaCMn2PGXfKKOGOGk18c3B1rTV6oXU2AATowZiYtRA\nNDSqoayuR2V1PSQSsaCT1oYPtoezvRlyi5R4Y/cF7uu+7jYI8esPZ3szONubw8HW9JFJxbToQfjt\nRj6yCirwzr7reGdtNFda9MvFe1xCtDAuQPAE+2GG+9hj57poHP4tE0cv3UNukZJLsMP8HbhNSdqy\nt5bhiQh3PBHhjqT0IiR8chmlFbX4f18kst+3kXV5YA0ALomqqVMhI0/R5nPh6KV7aGjaw/DUSA/c\nyirD5dRCXE4tbJFkF5VW458fX+Ra3MVGemDOk75cHfPW5aNw+hr7CctTIz069Po1dYwXTl3NRUFJ\nFVb9+wxy7rP3vTAuADHhrTe3amhKRjSlEBOjBnJlUJZmxlg0OQBznvTBb0n5+OViFrcvoVHFJtiO\ntqZYPKXrm6vb03z1X6Gsa7Xxsy2nr+VyrTWH+9hj4eSAR9yia5z6mcHVwRzZhZW4mFLYIsnOlytx\n/Ao7UGnGuEG8DKiSSMRYOXs41r1zFmIxO5q++euDl7MVfN1tkJZVip/OZbV4/S5R1ODbE3dw/Eo2\n93ceOqhfiw4f+sBtbO3ESrZmLw/A7itpr92it6s1V/IBAIsnB3Sq9NK1vzkG9DNFXnEVziXnc0l2\nRp6Ca8v3WBB/i2q6xluS/cILL2D16tVQqVSYNGkSfH19sXnzZuzfvx/vvfceX7+m03x8fPD111/r\n7fd3lkql5q7eooOdH/nCoa8EG3jQwg9gk+yOJmiJafex98gtAMCdnHJs+PACXl0Qiu9Osi3rxo5w\nabUqIhaL8MKsYVj1nzOoqKrHJ4dT8dP5LPx1kn+bNbcdxTDMg2E0XRgoI5GIMXWMF3Z9l4yz1/Mw\n9wnfdldna+sakdw0cvhhNYZ88na15so4/ncus1WSfe12EVd7PFngThyGBmJYW0jb/Eifb2KxCHGj\nPPHet+xmMSNDCebH+iE20qPTKyKGBmKsmBWEtW+fRb68Cu/uT8KwwfZQqRh8dPB3AOxHwk+NdOf7\nNDp5nBJMHeOFKaM9cTe3HKeu5kJeXoPFk4VJzoK87ZGwNBIbP7rIlYxNj/bSqv+xg60MtpZSlChq\nkZIhh5ezFS6kFOBWVikcbE3h7miBn5r2LYwb4QpTE0OE+PXH5dRCJKUXc4Oh7hVU4B8fnEd5ZR3E\nYhGenxqIJ//Umk8sFmHsCFeMfUSf8OY8nCwxdJAdbtyRcwn20xMGP3LDn6ZkpFHFwM7KBHOe8Gn1\nM1IjA4wPdcX4UFdU1zY0tTerQqmiFiH+DoJNObUya9lLub0km2EYnEvOx3++YrucDHa1xvp5IYL2\nvA4PcER2YSUupBRgYZw/95731ZHbUKsZ2FhIW/1tteHmYMGVgrS1GfWpke5IyypFYlohikqrYW8j\nQ01dI9a/ew4FJWxSaGQgRvQIl3Y78ejKg3KRjq1kNzSq8OnhVADsnpn2+lcDbA7yl2gvbNt7DSOH\nOCLsEV2E2rr9yCFO2H/iDi78XoAFk9i/8W9NeVB/GxkGdWCjaXfF27N2woQJOHv2LO7fvw9fX/aB\nFR8fj+eeew62trqf3tVTJd+Vc7VxowSYEMYnc9mDmtOODqS5X8qOLgbAbXK6nV2Gv289hYoqtmTh\nYXV19jYy7FwXjb1HbuPoxSzcL63Gls+uYPGUgA6PEf6z+kY11+6pK+UiADAuxBVfHbmNcmUdDp7N\nwJJ2VpyS7hRzA2ZC/Np/8eLLxCgP/Pur67iUUoCisuoWF0OHmjYt+Q+07Zbtj7QxdoQLbqQXo1Gl\nxoJJ/lqVVA1yscaU0V44cPouTl3NxamrDyaL2VlKsWLWML1e8DYnEokwyMWaa50lJG9Xa7z19yi8\n+dkVmEoNO9wj/WFEIhECBtrhzPVcnL6WizPX81q169KIbeoEonke1dazg6EGuVrh/z65hPLKOpgY\nG+DleSG89rGfMtoLN+7Im/7fE/ETHt3T20xmhCfC3XEiMRsvzAp6ZL2+TGoIDyfLDpWfacvc1Iib\nPNpeXfbvd+X44uc0rjzG2d4Mry8KF3xTX0SAI745no6i0mpk5ldg4ABLZOYrcOY6+xyc9bh3lxZI\n2tPeQkDkUCfsPpSCiqp6/HIxC/Oe8sPHP6SgoKQKErEIT8f4ICbcrdM9tYVi3TT1saKqHo0q9UMv\niFRqBhm55Thy8R4KSqogFgELml3UtGdMsAt8PWxhZynt0uvgyEA2yb5fWo2fzmdhzHBn/NrUfSpq\naNtljj0Fr88OMzMznDt3DseOHcO8efNQXFwMC4uetRNU3zQvHF4uVoJ+nM4HiUQMc5khKqsbOjRa\nvaFRhS2fX4GypgFmJobY/LcoJKUXYef+G9xKWHSwc7sbJqzNpVg2fSgmRnrgve/YSXifHk7lJul1\nVvONUJ3d+KhhbCjBxMc8sOfnWzh66R7iHx/80A4ol5tKRXzcbHT2Ihw1dAA+/uEm+6ZwIYurMczM\nVyCpaVW9rQESPZ2xoQQvPxvC2/3NjhmMvGIlMvIUqG9gB11IjQywbu4ImHdhCEhv4WxvjnfXjeXt\n/vw9bXHmei4y8yu4rw0cYInSilouCYwIdOQGa9hamsDT2RIZuQrhPMVjAAAXzElEQVRcvFmAQ79m\ncIMw/vlcxCOH53RWsI89Fsb5Qyxi9zB0NAF4btoQLJ7C70RBPkjEIliYGaP8IVMfGxrV2LonkSuL\nAgA/DxusfWYE752e2uLpbAk7KxPIy2twMaUABhIRNuy+AIZhF2oeD314mY4QDA0kmBDmhm9P3sGx\nS9nwdLbCkYvs5sunY7TbfCkEzdRHhmHLgf5c965WM3j/QDLOJuVxm/EB4PEwt05d5HW0Jr8tns6W\n6G8jw/3Sarx/IBm7D/7OdZvpyaUiAI9Jdk5ODubPn4/GxkbI5XJMnToVX375JS5duoRPPvkEfn6d\naxnVF9U1qLjNQ3yO4BWShakRKqsboOhAh5HdB1O4lkhrnglGfxsZYsLdoVIz2PVdMgwk4g6/QLk5\nWuC1heFYse0Uistq8K89idi+cnSne0prpj0CXSsX0XhqpAe+PXEHtfVsG77ZbaxuqdUMrqSyLc4e\nNZiDT0aGD94UjlxkLwIaGtX4pGlzraOtqU6Pp6eSGhngHwvC9H0Yvd4w737cyqqvuw2ejfXjNpuW\nVdaiqLQa7n968w/xdUBGrgK/XMjiPpl6ftoQ3hNsgF1tnzK6a9MKu1uCrWGlSbLb6JV9MjGHS7C9\nnC0x50lfDB9sr7PVRZFIhPAABxz+LRMnrmTjf+cyUVFVDxNjCdY8E9zhjZp8eiLCHd+duoNyZR22\nNu1H8HW3wV8EmKysLc1KNgCUVbROslP+kLfopmJnZYJwfwfMfUp3pS4iETtwbN/xdCSlF3Gdn5zs\nTPW2z4UvvCXZCQkJiIyMxIYNGxAczG7+2r59O1599VW8+eab3XIiZHeTmHofNXWNEIl6ztWbhakx\n8oqrHtnG79TVHO6JPGu8d4s6r6dGesDd0QJGBpJWE7naY2ZiiNWzh+OVXedwr7ASn/2U2un60+Yr\n2V3Z+KhhLjNCTLg7Dp3NwKEzd+FgK8PoYc4t3lTTc8q4N7HO1q1p68kIdxw4dYetZ//xJi7eLIS8\nnJ38NzXaS+tOJoTwxcHWFP+3NBJg2CmQzZM5a3Npi6RBI9S/P74+dptLsGPC3drdiEhasjI3Bgpa\nl4uoVGp8e5JtzRg5xAkvzRuhl4/uIwIdcfi3TBQ1TSs1NTHEP5dE6K3Erb+NDME+bOcmlZqB1EiC\nVbOH8zY6nU8WpkYQi0VQq5k267I1Cz8D+pnh9YVhcLQz1cvf2MfdBm8sCoeyuh6XUwuRllWGcSNc\nenSpCADw9oi4evUq5s+fD7H4wV0aGBhg6dKlSElJ4evX9GqaUpEhXnYPnajW3XSkV3ZWQQV27mc3\noAUN6ofZMa03/fh52HZoitafBXjacf07fzj7B04mZrcYwfoomk2PQNfLRTSmjPaEqYkhqmobsX3v\nNby441fcvvdgzLimVMTRjm0Zp0v2NjJutfrwuUzIy2tgIBFjfqwfnqBkhHQzgZ52CPSy6/AbrOcA\nK9g0fSw+2M0az00VZrNnb/WwXtlnrueisKQaABA/YbDeEh5/D1uuJMvC1Aibl0bqfQ9JbOSDzZaL\nJgd2euKvrojFIm5zq6bjUHNXUtn3pZFDHOHUz0zvSa2ZzAhjR7hi2fShXKeRnoy3JNvIyAgVFRWt\nvp6bmwtT0+754OtOlDUN3BVlTykVAZqNVn/ISN7q2ga8+ell1DeoYGspxdo5wbyvms6e4MMl6P/+\n6jqe33Ic35++C2X1o0tYWqxka7l5xs7KBNtXjuJWqW9nl2HtO79i296rKFHUcK37OjJiWggTIx+0\n0vRytsR/Vo/GX8YO0vuLKiHaYrsPDceTI93x6vxQXlp79iWaTweal4uo1Ay+Oc52fAoPcOjSnhe+\nSCRiLJ02BBGBjtiyLKpblBAE+9hjzpM+mB/rhwlh/E12FYKmLrv8TyvZecVK5BWzHVEe1UWEdA1v\n5SJxcXHYtGkTNm7cCABQKBT4448/sHHjRsTGxvL1a3qtIxfYUb+GBmJBBg4IxbKd0eoMw+CdfUnI\nl1dx44aF2OxnaCDG+nkheHvfdSTflaOwpBr//fEmDp7JwPaVo9qdkNY8ye5sPXdbnOzM8I8FYUhK\nL8LuQynILqzE6au5uPB7Afe7dF0qojHUux9emBWERhWD8aGugrbdIkTXhvvY89pFpC/RrHQ2Lyc4\nfyMfecVse89Z4x/dQUVojw0b8NCptfogEom6RVw6gr2IUrTqla1Z2DOXGXJ92wm/eHuXXbNmDcLC\nwvD000+jpqYG06dPx7JlyzBu3DisXbuWr1/TK129dR+f/5wGgG1XY2qiu/Ht2mqvXOTY5WycS2bb\n8CyY5C/oRz/2NjIkLI3EjrXRiAl3g5GBGKUVtdi+91q75SOajY9GhhJeNyUFedvjndVj8PzUQJjL\nDLkE28zEEL4e+nsxGx/qhici3CnBJoRw/lwuolYz2Hf8NgB2hbMrpXyk+9BMfSz909RHTalIsG9/\n2pcjEF7eaW/fvo179+5h3bp1uHLlCn788UccPHgQly9fxrRp0zBv3jw+fk2vlJmvwFufJ0KtZuDu\naIHnpw3R9yF1yoMku+UVcmFJFT46xA7qCPN34GXkbUe4O1rg7zOCsDJ+OAC277hmyE1btBlE8ygS\niRixUQPxwfrxmPTYQBgZiDExaiAluISQbkWTZFdWN6CxaSCaZlLmrG7Wko50nqbvd/Oa+6qaBm6q\nY6gvdZcSilblIhkZGfjb3/6G7Gx2tOmgQYOwe/duDBo0CEqlElu3bsU333wDZ+eeU2OsS6UVtfjn\nx5dQU9cIa3NjvLYw7JFDCrobTflHZVU91GoGYrEIKjWD/3x9HTV1KliaGeHvM4J0Xvf72LABuJ5e\nhGOXs/HlkVsY4mXX5kq6ZoVZaixcDae5zAhLpgRiUVxAt23hRQjpu6ybjf5OvivHu98mAWDbKfaG\nzWd9nbV563Kg6+lFUDW9Zw+jMivBaLWklpCQADMzM3z55ZfYt28f+vXrh02bNiEjIwNxcXH4/vvv\nsWzZMvz44498HW+vsvvg75CX18DIUILXFoZ1eCx5d6JZyVYzQFUt28j+h7MZ3BXysulDuVUSXVsy\nJRDO9mZQqxls3ZMIZbNG+xq19cKtZP8ZJdiEkO6o+Wj1tz6/gpo6FWwsjPFC/DA9HhXhi2Ylu7Si\nDkxTn0tNPba/hy3MelCJak+j1Up2cnIyPvzwQwwfzn40v3nzZsTExCA9PR3Ozs747LPP4OLiwsuB\n9kb21jKYmRhiZfwwnYxAFkLziV8KZR0K5FX4/Ce2vnzsCBe9buKUGhvgxbkjsPo/Z1FUVoNfLmRx\n7f406hrYmuyujlQnhJCezsLUCCIROxWwurYRBhIx1s8PbXfTOOk5NCvZ9Q0q1NQ1wtjIAIlpbJId\n4kddRYSkVZJdVVUFV9cHrWv69+8PhmEwbNgwbNmyhVqDPcJfJ/ljXqxfj95w0DzJ/uXCPfx8ge2S\nYmdlgiVT9N+r1sPJEtHBzjh2ORuJafdbJ9machEtBtEQQkhPJpGIYWlqzLXw+/uMoYJMyyT60WLq\nY2UdyisruGYFlGQLS6tyEYZhWiXSYrEYCxcupAS7g3pygg0AJsYG3Ea+Q2czUN+ggp2VCV5fGNZt\nuqRo+n+mZZW26p1dp8NyEUII6a48nNg+2JNHeWJcSPfu+0w6p3nN/TfH07Hxo4sAAEdbUwzop9vB\naH2NIMt3Jib0EVNfIRKJYGlmhBIFu6HCx80ar/w1tM3Rx/oS5N0PkqYNmddvF7fotcp1F6FyEUJI\nH7bmmWBkF1YiwNNW34dCeCY1NoCJsQFq6hpxMjEHAGAqNcCSqYG0ICowrZPsQ4cOtZjoqFarcfjw\nYdjYtPyoadasWdr+KtJNeQ6wQomiEONCXLBs+tBuN21NJjWE/0BbJN+VI/HW/RZJdm0dJdmEEGJp\nZoxAL/1sUifCs7MyQc79SohEwPgQV8x7yk9vTQn6Eq2SbCcnJ+zZs6fF12xtbbF///4WXxOJRJRk\n92IvzhuBwpIquPY377ZXxSN8+yP5rhxXb93nWg0CzTY+UrkIIYSQXmr+RD/8mpSHSVED4e3aMxst\n9ERaJdknT57k6zhID2ZsKIGbg4W+D6NdI3z7478/3oRCWY+7ueXciwxtfCSEENLbhfo5INSPhs7o\nGo2eI32Cs70Z7G3YPuSa1kVAsz7ZVC5CCCGEEB5Rkk36BJFIhJCmLiPNk2whx6oTQgghpO+iJJv0\nGZpWfndyyrnxsnX1bE22lFayCSGEEMIjSrJJnxHgaQsjA/Yhf/12EYBmfbIpySaEEEIIjyjJJn2G\n1MgAgV52AIDzyQUAmvfJpo2PhBBCCOEPJdmkT4kayvbIvnSzECkZ8gcbH6kmmxBCCCE8oiSb9CnR\nI1wwcIAlAOD9A8loaFQDoHIRQgghhPCLkmzSp0jEIiydNgQAcK+wkvs6bXwkhBBCCJ8oySZ9jo+7\nDcaHuLb4GpWLEEIIIYRPlGSTPunZWD+Ymhhy/5Ya08ZHQgghhPCHkmzSJ1mZG2PuEz7cv02lhu38\nNCGEEEJI59DyHemznhjpgXJlPUxNDGBlbqzvwyGEEEJIL0JJNumzJGIRnmm2mk0IIYQQwhcqFyGE\nEEIIIYRntJLdCSoVO7iksLBQz0dCCCGEEEJ0QZP3afLAjqIkuxOKi4sBAM8884yej4QQQgghhOhS\ncXEx3NzcOvzzlGR3QkBAACIiIrBx40ZIJN2vr3JOTg7mz5+PTz/9FC4uLvo+nDYlJCTg1Vdf1fdh\ntInipx2Kn3Yoftqh+GmH4qcdip92unv8VCoV3njjDQQEBHTqdpRkd4JUKoWtrW2nrmJ0qaGhAQDg\n4OAAZ2dnPR9N22QyWbc9Noqfdih+2qH4aYfipx2Kn3YoftrpCfGztbWFVCrt1G1o42MnTZgwQd+H\n0KNR/LRD8dMOxU87FD/tUPy0Q/HTDsVPO12JHyXZnRQTE6PvQ+jRKH7aofhph+KnHYqfdih+2qH4\naYfip52uxI+SbEIIIYQQQngm2bBhwwZ9HwThj1QqRWhoKExMTPR9KD0SxU87FD/tUPy0Q/HTDsVP\nOxQ/7fTG+IkYhmH0fRCEEEIIIYT0JlQuQgghhBBCCM8oySaEEEIIIYRnlGQTQgghhBDCM0qyCSGE\nEEII4Rkl2YQQQgghhPCMkmxCCCGEEEJ4Rkk2IYQQQgghPKMkuxtLTEzEjBkzEBwcjPHjx+Prr78G\nACgUCixbtgzBwcEYM2YM9u/fz92mvr4er7zyCkJDQzFy5Ejs2rWr1f2q1WosW7YMe/bs0dm56APf\n8VMoFFi1ahVCQ0MRGhqKdevWQalU6vy8dIXv+DEMg2HDhrX4b9GiRTo/L13hO36xsbEtYhcYGIjB\ngwfj/v37Oj83XeA7ftXV1XjjjTcQERGByMhIbN26FY2NjTo/L13pSvw06urqMHPmTJw6darN+964\ncSP+9a9/CXr8+sZ3/Orr67FhwwaEh4cjODgYS5cu7bXPXUCYx19sbCyGDh3KvQbGxsbq5Fy0wpBu\nqby8nAkJCWEOHTrEqFQqJiUlhQkJCWHOnTvHLF++nFm7di1TW1vL3LhxgwkNDWXS0tIYhmGYLVu2\nMM8++yxTUVHBZGZmMtHR0cyJEye4+83Ly2MWL17MeHt7M1988YW+Tk9wQsRvzZo1zKpVq5iqqipG\nqVQyCxYsYDZv3qzP0xSMEPHLzMxkgoKCGLVarc9T0wmhnr8aKpWKmTt3LrN9+3Zdn5pOCBG/N954\ng5k6dSpTUFDAKBQKZuHChcxbb72lz9MUTFfjxzAMc/v2bWbmzJmMt7c3c/LkyRb3W1JSwqxdu5bx\n9vZmtm7dquvT0hkh4rd9+3Zmzpw5TFlZGVNXV8e8/PLLzLJly/RxeoITIn41NTWMr68vU1JSoo9T\n6jJaye6m8vPzMXr0aMTFxUEsFsPf3x9hYWG4du0ajh8/jhUrVsDY2BhDhgzBxIkTuavBH374Ac89\n9xzMzc3h7u6OOXPm4JtvvgHAXklPnToV3t7eGDZsmD5PT3BCxO/NN9/Eli1bYGJiArlcjurqalhb\nW+vzNAUjRPxSU1Ph4+MDkUikz1PTCSHi19znn38OpVKJFStW6PrUdEKI+B09ehQrV66Eg4MDLCws\nsGLFChw4cABMLxx63NX45eXlYe7cuYiJiYGTk1Or+42Pj4dUKsX48eN1fUo6JUT8VqxYgd27d8PK\nygolJSWoqqqi949OxC89PR12dnawsbHRxyl1GSXZ3ZSvry+2bt3K/VuhUCAxMREAYGBgABcXF+57\nHh4euHPnDhQKBeRyOby8vFp9T3O7w4cPY+3atTA0NNTRmeiHEPEzNDSEkZER1q9fj5iYGCiVSsTH\nx+vojHRLiPilpaVBqVRi8uTJiIiIwIoVK3rtx6VCxK/5fe3cuROvv/46JBKJwGeiH0LET6VSwcTE\nhPueSCRCWVkZFAqF0Kejc12JHwBYW1vj+PHjWLBgQZsXw1988QU2bdrUIo69kRDxk0gkkEql2LFj\nB6Kjo5GUlIQlS5bo4Gx0T4j4paamwsDAALNmzUJ4eDgWLFiAjIwMHZyNdijJ7gEqKyvx/PPPc1eD\nUqm0xfelUilqa2tRU1MDAC1eADXfAwCxWIx+/frp7sC7Cb7ip7Fx40ZcuXIFHh4eWL58ufAnoGd8\nxc/IyAhBQUH4+OOPcfToUchkMoofOv/427t3L4YOHYqgoCDhD74b4Ct+Y8eOxc6dOyGXy6FQKPD+\n++8DYOs/e7OOxg8AZDIZzM3NH3pf/fv3F/RYuyM+4wcAS5YsQVJSEiZMmICFCxeioaFBsGPvDviM\nX2BgILZt24bTp08jICAAixcvbvX62N1Qkt3N5eTkID4+HpaWlti5cydkMlmrB1VtbS1kMhn34G3+\nfc33+ioh4mdsbAxzc3OsW7cOly9fRnl5ufAnoid8xm/58uXYtGkT7OzsYG5ujpdeegk3btxAUVGR\n7k5Ix4R4/B04cACzZ88W/uC7AT7j9+qrr8LJyQlxcXGIj4/Hk08+CQCwsLDQ0dnoXmfiR1oTIn7G\nxsaQSqV48cUXkZ+fj/T0dL4Pu9vgM37x8fF4++234ezsDKlUilWrVkGhUCAtLU2ow+cFJdnd2M2b\nNzFz5kxERUXhvffeg1QqhZubGxobG5Gfn8/9XGZmJry8vGBlZQVbW1tkZma2+J6np6c+Dl/v+I7f\nggULWux2bmhogIGBQa99g+I7fh9++CFu3rzJfa++vh4A+6bTGwnx/M3IyIBcLseoUaN0ei76wHf8\nioqK8NJLL+H8+fP4+eefYWFhAXd3915b+tDZ+JGW+I7f+vXrsXfvXu7fKpUKarW6117k8R2/ffv2\n4fz589y/VSoVGhsbu/37ByXZ3ZRcLseiRYvw17/+FevXr4dYzP6pzMzMMG7cOGzbtg01NTVITk7G\n4cOHMWnSJABAXFwcduzYgfLycmRlZWHPnj2YPHmyPk9FL4SIn5+fH3bt2oXS0lIoFAq89dZbiIuL\ng5GRkd7OUyhCxO+PP/7Ali1bUFZWhsrKSiQkJGDcuHGwtLTU23kKRajnb1JSEvz9/XvlY645IeL3\n0UcfISEhAfX19cjNzcW2bdt67ScCXY0fYQkRvyFDhuC///0vcnNzUVNTg4SEBAQHB7eoT+4thIhf\nUVEREhISUFBQgNraWmzZsgUDBw6Ej4+P0KejHX23NyFt27VrF+Pt7c0EBQW1+G/79u1MWVkZs2LF\nCiYkJIQZPXo0s3//fu52NTU1zGuvvcaEh4czERERzK5du9q8/zlz5vTqFn5CxK+uro7ZtGkTExER\nwURGRjIbN25kqqqq9HF6ghMifpWVlczLL7/MhIWFMcOHD2dWr17NlJeX6+P0BCfU8/ftt99mVq5c\nqevT0Tkh4ldSUsI8//zzTHBwMBMVFcW8++67vbadZFfj11x0dHSrFn4aa9as6dUt/ISIn1qtZnbs\n2MFERUUxYWFhzOrVq3tcO7qOEiJ+9fX1zObNm5nIyEgmKCiIWbx4MZOXl6erU+oyEcP0wv5FhBBC\nCCGE6BGVixBCCCGEEMIzSrIJIYQQQgjhGSXZhBBCCCGE8IySbEIIIYQQQnhGSTYhhBBCCCE8oySb\nEEIIIYQQnlGSTQghhBBCCM8oySaEEEIIIYRnlGQTQgghhBDCs/8PnGu/lqTnHaIAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1258ef1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "smt.seasonal_decompose(y).plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a few ways to handle seasonality.\n",
    "We'll just rely on the `SARIMAX` method to do it for us.\n",
    "For now, recognize that it's a problem to be solved."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ARIMA\n",
    "\n",
    "So, we've sketched the problems with regular old regression: multicollinearity, autocorrelation, non-stationarity, and seasonality.\n",
    "Our tool of choice, `smt.SARIMAX`, which stands for Seasonal ARIMA with eXogenous regressors, can handle all these.\n",
    "We'll walk through the components in pieces.\n",
    "\n",
    "ARIMA stands for AutoRegressive Integrated Moving Average.\n",
    "It's a relatively simple yet flexible way of modeling univariate time series.\n",
    "It's made up of three components, and is typically written as $\\mathrm{ARIMA}(p, d, q)$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ARIMA stands for AutoRegressive Integrated Moving Average, and it's a relatively simple way of modeling univariate time series.\n",
    "It's made up of three components, and is typically written as $\\mathrm{ARIMA}(p, d, q)$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [AutoRegressive](https://www.otexts.org/fpp/8/3)\n",
    "\n",
    "The idea is to predict a variable by a linear combination of its lagged values (*auto*-regressive as in regressing a value on its past *self*).\n",
    "An AR(p), where $p$ represents the number of lagged values used, is written as\n",
    "\n",
    "$$y_t = c + \\phi_1 y_{t-1} + \\phi_2 y_{t-2} + \\ldots + \\phi_p y_{t-p} + e_t$$\n",
    "\n",
    "$c$ is a constant and $e_t$ is white noise.\n",
    "This looks a lot like a linear regression model with multiple predictors, but the predictors happen to be lagged values of $y$ (though they are estimated differently)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Integrated\n",
    "\n",
    "Integrated is like the opposite of differencing, and is the part that deals with stationarity.\n",
    "If you have to difference your dataset 1 time to get it stationary, then $d=1$.\n",
    "We'll introduce one bit of notation for differencing: $\\Delta y_t = y_t - y_{t-1}$ for $d=1$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [Moving Average](https://www.otexts.org/fpp/8/4)\n",
    "\n",
    "MA models look somewhat similar to the AR component, but it's dealing with different values.\n",
    "\n",
    "$$y_t = c + e_t + \\theta_1 e_{t-1} + \\theta_2 e_{t-2} + \\ldots + \\theta_q e_{t-q}$$\n",
    "\n",
    "$c$ again is a constant and $e_t$ again is white noise.\n",
    "But now the coefficients are the *residuals* from previous predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combining\n",
    "\n",
    "Putting that together, an ARIMA(1, 1, 1) process is written as\n",
    "\n",
    "$$\\Delta y_t = c + \\phi_1 \\Delta y_{t-1} + \\theta_t e_{t-1} + e_t$$\n",
    "\n",
    "Using *lag notation*, where $L y_t = y_{t-1}$, i.e. `y.shift()` in pandas, we can rewrite that as\n",
    "\n",
    "$$(1 - \\phi_1 L) (1 - L)y_t = c + (1 + \\theta L)e_t$$\n",
    "\n",
    "That was for our specific $\\mathrm{ARIMA}(1, 1, 1)$ model. For the general $\\mathrm{ARIMA}(p, d, q)$, that becomes\n",
    "\n",
    "$$(1 - \\phi_1 L - \\ldots - \\phi_p L^p) (1 - L)^d y_t = c + (1 + \\theta L + \\ldots + \\theta_q L^q)e_t$$\n",
    "\n",
    "We went through that *extremely* quickly, so don't feel bad if things aren't clear.\n",
    "Fortunately, the model is pretty easy to use with statsmodels (using it *correctly*, in a statistical sense, is another matter)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EnU4X03GtXr0aK1eu7MtbIxIEn9CH10RtyQiCIAiC6AV9ErwinZ2dWLJkCXd533jjDcXz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\nGDVvTHPg+69Pn6SMDGv3yW7x2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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ca33f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mod = smt.SARIMAX(y, trend='c', order=(1, 1, 1))\n",
    "res = mod.fit()\n",
    "tsplot(res.resid[2:], lags=24);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Statespace Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>fl_date</td>     <th>  No. Observations:  </th>    <td>193</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 1, 1)</td> <th>  Log Likelihood     </th> <td>-1494.618</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Sun, 03 Sep 2017</td> <th>  AIC                </th> <td>2997.236</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>11:12:22</td>     <th>  BIC                </th> <td>3010.287</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>01-01-2000</td>    <th>  HQIC               </th> <td>3002.521</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 01-01-2016</td>   <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th> <td>   -5.4306</td> <td>   66.818</td> <td>   -0.081</td> <td> 0.935</td> <td> -136.391</td> <td>  125.529</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>     <td>   -0.0327</td> <td>    2.689</td> <td>   -0.012</td> <td> 0.990</td> <td>   -5.303</td> <td>    5.237</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>     <td>    0.0775</td> <td>    2.667</td> <td>    0.029</td> <td> 0.977</td> <td>   -5.149</td> <td>    5.305</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>    <td> 3.444e+05</td> <td> 1.69e+04</td> <td>   20.392</td> <td> 0.000</td> <td> 3.11e+05</td> <td> 3.77e+05</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (Q):</th>          <td>225.58</td> <th>  Jarque-Bera (JB):  </th> <td>1211.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                 <td>0.00</td>  <th>  Prob(JB):          </th>  <td>0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th>  <td>0.67</td>  <th>  Skew:              </th>  <td>1.20</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>     <td>0.12</td>  <th>  Kurtosis:          </th>  <td>15.07</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Statespace Model Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:                fl_date   No. Observations:                  193\n",
       "Model:               SARIMAX(1, 1, 1)   Log Likelihood               -1494.618\n",
       "Date:                Sun, 03 Sep 2017   AIC                           2997.236\n",
       "Time:                        11:12:22   BIC                           3010.287\n",
       "Sample:                    01-01-2000   HQIC                          3002.521\n",
       "                         - 01-01-2016                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "intercept     -5.4306     66.818     -0.081      0.935    -136.391     125.529\n",
       "ar.L1         -0.0327      2.689     -0.012      0.990      -5.303       5.237\n",
       "ma.L1          0.0775      2.667      0.029      0.977      -5.149       5.305\n",
       "sigma2      3.444e+05   1.69e+04     20.392      0.000    3.11e+05    3.77e+05\n",
       "===================================================================================\n",
       "Ljung-Box (Q):                      225.58   Jarque-Bera (JB):              1211.00\n",
       "Prob(Q):                              0.00   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               0.67   Skew:                             1.20\n",
       "Prob(H) (two-sided):                  0.12   Kurtosis:                        15.07\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's a bunch of output there with various tests, estimated parameters, and information criteria.\n",
    "Let's just say that things are looking better, but we still haven't accounted for seasonality.\n",
    "\n",
    "A seasonal ARIMA model is written as $\\mathrm{ARIMA}(p,d,q)×(P,D,Q)_s$.\n",
    "Lowercase letters are for the non-seasonal component, just like before. Upper-case letters are a similar specification for the seasonal component, where $s$ is the periodicity (4 for quarterly, 12 for monthly).\n",
    "\n",
    "It's like we have two processes, one for non-seasonal component and one for seasonal components, and we multiply them together with regular algebra rules.\n",
    "\n",
    "The general form of that looks like (quoting the [statsmodels docs](http://www.statsmodels.org/dev/examples/notebooks/generated/statespace_sarimax_stata.html) here)\n",
    "\n",
    "$$\\phi_p(L)\\tilde{\\phi}_P(L^S)\\Delta^d\\Delta_s^D y_t = A(t) + \\theta_q(L)\\tilde{\\theta}_Q(L^s)e_t$$\n",
    "\n",
    "where\n",
    "\n",
    "- $\\phi_p(L)$ is the non-seasonal autoregressive lag polynomial\n",
    "- $\\tilde{\\phi}_P(L^S)$ is the seasonal autoregressive lag polynomial\n",
    "- $\\Delta^d\\Delta_s^D$ is the time series, differenced  $d$ times, and seasonally differenced $D$ times.\n",
    "- $A(t)$ is the trend polynomial (including the intercept)\n",
    "- $\\theta_q(L)$ is the non-seasonal moving average lag polynomial\n",
    "- $\\tilde{\\theta}_Q(L^s)$  is the seasonal moving average lag polynomial\n",
    "\n",
    "I don't find that to be very clear, but maybe an example will help.\n",
    "We'll fit a seasonal ARIMA$(1,1,2)×(0, 1, 2)_{12}$.\n",
    "\n",
    "So the nonseasonal component is\n",
    "\n",
    "- $p=1$: period autoregressive: use $y_{t-1}$\n",
    "- $d=1$: one first-differencing of the data (one month)\n",
    "- $q=2$: use the previous two non-seasonal residual, $e_{t-1}$ and $e_{t-2}$, to forecast\n",
    "\n",
    "And the seasonal component is\n",
    "\n",
    "- $P=0$: Don't use any previous seasonal values\n",
    "- $D=1$: Difference the series 12 periods back: `y.diff(12)`\n",
    "- $Q=2$: Use the two previous seasonal residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mod_seasonal = smt.SARIMAX(y, trend='c',\n",
    "                           order=(1, 1, 2), seasonal_order=(0, 1, 2, 12),\n",
    "                           simple_differencing=False)\n",
    "res_seasonal = mod_seasonal.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Statespace Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>               <td>fl_date</td>            <th>  No. Observations:  </th>    <td>193</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 1, 2)x(0, 1, 2, 12)</td> <th>  Log Likelihood     </th> <td>-1357.847</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                   <td>Sun, 03 Sep 2017</td>        <th>  AIC                </th> <td>2729.694</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                       <td>11:12:25</td>            <th>  BIC                </th> <td>2752.533</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                    <td>01-01-2000</td>           <th>  HQIC               </th> <td>2738.943</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td>- 01-01-2016</td>          <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>               <td>opg</td>              <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th> <td>  -17.5871</td> <td>   44.920</td> <td>   -0.392</td> <td> 0.695</td> <td> -105.628</td> <td>   70.454</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>     <td>   -0.9988</td> <td>    0.013</td> <td>  -74.479</td> <td> 0.000</td> <td>   -1.025</td> <td>   -0.973</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>     <td>    0.9956</td> <td>    0.109</td> <td>    9.130</td> <td> 0.000</td> <td>    0.782</td> <td>    1.209</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L2</th>     <td>    0.0042</td> <td>    0.110</td> <td>    0.038</td> <td> 0.969</td> <td>   -0.211</td> <td>    0.219</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.S.L12</th>  <td>   -0.7836</td> <td>    0.059</td> <td>  -13.286</td> <td> 0.000</td> <td>   -0.899</td> <td>   -0.668</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.S.L24</th>  <td>    0.2118</td> <td>    0.041</td> <td>    5.154</td> <td> 0.000</td> <td>    0.131</td> <td>    0.292</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>    <td> 1.842e+05</td> <td> 1.21e+04</td> <td>   15.240</td> <td> 0.000</td> <td> 1.61e+05</td> <td> 2.08e+05</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (Q):</th>          <td>32.57</td> <th>  Jarque-Bera (JB):  </th> <td>1298.39</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.79</td>  <th>  Prob(JB):          </th>  <td>0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>0.17</td>  <th>  Skew:              </th>  <td>-1.33</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td>  <th>  Kurtosis:          </th>  <td>15.89</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 Statespace Model Results                                 \n",
       "==========================================================================================\n",
       "Dep. Variable:                            fl_date   No. Observations:                  193\n",
       "Model:             SARIMAX(1, 1, 2)x(0, 1, 2, 12)   Log Likelihood               -1357.847\n",
       "Date:                            Sun, 03 Sep 2017   AIC                           2729.694\n",
       "Time:                                    11:12:25   BIC                           2752.533\n",
       "Sample:                                01-01-2000   HQIC                          2738.943\n",
       "                                     - 01-01-2016                                         \n",
       "Covariance Type:                              opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "intercept    -17.5871     44.920     -0.392      0.695    -105.628      70.454\n",
       "ar.L1         -0.9988      0.013    -74.479      0.000      -1.025      -0.973\n",
       "ma.L1          0.9956      0.109      9.130      0.000       0.782       1.209\n",
       "ma.L2          0.0042      0.110      0.038      0.969      -0.211       0.219\n",
       "ma.S.L12      -0.7836      0.059    -13.286      0.000      -0.899      -0.668\n",
       "ma.S.L24       0.2118      0.041      5.154      0.000       0.131       0.292\n",
       "sigma2      1.842e+05   1.21e+04     15.240      0.000    1.61e+05    2.08e+05\n",
       "===================================================================================\n",
       "Ljung-Box (Q):                       32.57   Jarque-Bera (JB):              1298.39\n",
       "Prob(Q):                              0.79   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               0.17   Skew:                            -1.33\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                        15.89\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_seasonal.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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ATHCc8Pvcq/nzS4UTtNZPxaIM0v+lxwUAk8ylCPAoWpMyvDbwPI/Mc1X4bkdu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c8t+6L/q5bNljQUFBQoOjpa0dHReuSRR7R9+3bFxMRo79698vT8LcwkJCRo3bp1Wr9+verXr6/J\nkydr+fLlmjZtmlXqkXUhX25eeZV+X86ly/Kp2/C3/8+5/jE2/fOY0s9clHTlK/iNnx/Vn/q2rHyF\nrST7YoE+2/+zTv/3oho18FX/rs1Uz/e2392/su2t7P7163joTE6R+XWj+rV09jrv6RxQW+9t/1p1\n7/JXQ4OXIjo3ve7+la2PM6ns34XK/ln9fG1Azjh/3f2rJRfpl8xLN/XWu+vXsnJlKiclJUWzZs3S\njz/+qGbNmmnu3LkKCgoqs9+7776rdevW6dKlS+rTp49effVV+fj42KHGqCosHatZ6P+q5WIymUy2\nONGXX36p2bNn64svvjCXPfzww4qJiVH//v3NZc8995z8/Pz07LPPSpKSk5M1evRoHThwQG5ubhU6\nV1ZWlrKzsy3KMjIyNHr0aPn1mSYPn9tvvUEAUI6Plwy227kLCgrUr18/i4mFZcuWlTuxMGvWLIuJ\nBX9//1ueWEhPT1dERIRWvbNZd951900dI+1nowb2DZV0ZQ1v02Z+192/Oq0xr2xdKtvWyu6/Ztv/\nmgOUJDW/u851A9TM1f9WyVWJwNXVRfPGd7NafXB9le1/R9AuoL7NzmWzGV6j0Sh/f3+LMj8/Px0/\nftwi8Kampqpfv34W+1y4cOH/t3f3MVGc+x7Av7sgLGtRV4QLFFTUtli1raAYlQazfTE9BXraUkOr\noZhLi61X8TZqTv/SxDbRmHpbpJVYmligrUZyIzeSNrGU2KSp9RhNUWsN54AGLgctVwEpLG/73D9w\nV1ZeZGDnZZ/5fhLCzjCwv2defvzmeWZmcePGDcTGxo7rvcrLy1FUVOSfwImIAsSZM2dgtVrxxhtv\nAACysrLw5ZdfoqamxifPVlZWIisrCwkJgwVKQUEBcnNzsWPHjnF3LIxloiNpQGCPpimNRe2RNKWj\nLUpHcziS5l9K17+RKB059JjoSBqgfDRNs4K3q6sLYWFhPvNsNhtcLt9rVLq7u2Gz3Tsj9vxOd/f4\nN/qGDRuQnp7uM8/Tw5v30mLMihw8QMuqLvrsXHFRD/ktUSrd+P/1zXnfMzsL8J+vJ426/L/+twl5\nOa8AAEpK/xsxD8eNGc/QRAz4t60yqPvHdZ9LJjLSHh1zeyld/2our3Q/Ntq+oHTdKG3vZNb9Dz9U\nY/bsOeNqhxFo2bEw2kgaAJRUXsIUe/NEmwHnvxcDAL4+fRPATUW/23SzEx99fX7C7+1P44lFaVsn\ns27cAorWjRrx0/gZaV9WoulmJ774n8uqv4/S0TTNCt6wsLBhxa3L5Rp2zZjNZkNPT4932lPoTp06\n/kre4XDA4XD4zJsyZcrgz8JtiJg+WES33un3Waa59U/vzyYrYnoY3n11xriXnx09zWfoaXb0tDFj\naWkGnlz7H3DEJKLmYhvWx8WPOXSW/dxjhhn2M6Kv/3EHjpjBoulmex+q/9445tCf0vXf1RaMrvbB\nYmD61OAH7mdKlle6HxttX1C6bpS2dzLrftb0UN2vyVVCy44FjqQRUSDRrOCdN28eysvLfeY1NDQM\n64mdP38+6uvrfZYJDw9HVFSU32Oa/W/hvkWmjnf+ZzkfGVaEjOWH2jZExC0GAPzrVu8DL26fOc3G\ni9/HcKOtz2f6QU8iULr+1aR0Pw70fUFpezu6+rFy3QdwxCTixJlWrHfESHuyp2XHwnhH0pSOXiml\n5mia0tGBiQ7rmoWRR3PUWF7Nfd9osSsdOdRjJE2zgnflypXo7e1FWVkZsrOzUVlZidbWVqSmpvos\nl5mZiV27dmHt2rWIiYlBYWEhMjIyYLX6/wlqSotMNSktQpQWaDQ2pUWU2utfSZFmpP1YC2qfHAYy\nLTsWxjuSpnT0Sik1R9OUjuQojcVslK5PLUdz1FhezX1f7VFGpbErHTnUYyRNs+fwhoSE4PPPP0dV\nVRVSUlJQXl6OQ4cOwW63Iy8vD8XFg9cBOZ1OvPXWW8jPz8eaNWsQHh6OnTt3qhKTp8j8IH8V3v7r\nkoDq9bm/IONzaScny/kI5sZMg9VqwdyYaQ8sotRe/54izRoU7C3SRhPI+/FEKG2vmU4Oh3Ys9PX1\noaKiYtSOhWPHjqGurg6dnZ2qdywoObbUpiQeJcchPZjS9Rno/+fU3PfV3jeVxh4I/4c0/WjhxMRE\nHD16dNj8kpISn+mcnBzk5ORoFVZAMluvntqU9rCrvf7NVKSpzUiXLqnN07Gwe/duHDhwAHPmzPHp\nWFi2bBk2bdoEp9OJpqYm5Ofno6OjA2lpaap3LBiFknh4HPqX0vUZ6P/n1Nz31d43jXbc+oOmBS/5\nj4w7YyBRe/2bqUhTW6D/01SKHQv+w+PQv4x2v0EgX9/PfVM5TT9amIjGx2jDwIEsEIbayJh4HPqX\n0dan0ssCPAXyXwoqcOJMq64f/Wu0dRkI2MNLZEDswSfSH49D/zLa+gzkp/MYbV0GAvbwEhERkeko\nvSmO13QHNha8REREZDpGezoPqYuXNBAREZHpGO3pPKQuFrxEpKtAvlOaiMyD180GNl7SQES64sP9\niYhIbSx4iUhXvBGEiIjUxoKXiHTFG0GIiEhtLHiJSFd8gDoREamNN60Rka54IwgREamNBS+RBvgk\nAiIiIv3wkgYiDfBJBERERPphwUukAT6JgIiISD8seIk0wCcREBER6cd0Ba/VavHLl8UCWCwAhn4R\njYJPIiAiUpfnXom/FFTgxJlW3Opw6R0SGYjpblp7JH4G4uIiVH0PIcSQ16q+1bhMNgQhBIS4+x2A\ncAu47zbMLQAIAffdn7uFgNstMOAe/O55PeB97R6cHrj7NwywfrTAJxEQEanLc68EAO+9Esy75GG6\nglcLFotlyGsdA/Eb9RohHnBGMNkTBrcYWmzfK7pHKsrdQwp5tQgxGJPnJMItBIQbELg3bZaTAJLX\nY3MciIubpXcYZDI377tXovHGHSxZwP0QAOyWdu/rx+bOxPz5+q6XofFohQUv6crygDOCyZ4wWGFB\ncNDk/obWxN0ec9ztUR/+8xF/S92gJkjp+cNgsweLfu/ru39H3H0hPNMqnZwMdE1V5e8SkboS587E\n5fr/85mmQa3tvd5HYx48UY+/5UYjOsJcuY4FL5HBWCwWBFkAXhiuj/CpoXqHQEQTsC17KT4+egG/\nX7uFxLkzsS17qd4hGcY3NU3eyz3+2dyFj49ewN7NqTpHpS0WvERERBTwoiOmmq6IG69rLd0+079f\nu6VTJPrR9CkNR44cwdNPP42kpCRs374dXV1dIy7X0tKCd999FytWrMDq1auxZ88e9Pb2ahkqERER\nkRTuv7zDjJd7aFbw1tTU4IsvvkBpaSlOnz6N9vZ2FBYWjrjsjh07EB0djR9//BEnTpzAxYsX8emn\nn2oVKhEREZE0tmUvxaJ5EQiyWrBoXoQpL/fQrOCtrKxEVlYWEhISEB4ejoKCAlRUVGBgYMBnud7e\nXoSFheGdd95BaGgoIiMjkZGRgQsXLmgVKhFRwOJIGhHdz3O5x4n9mdi7OdV0N6wBfi54+/v70dHR\nMeyrs7MT9fX1WLBggXfZhIQE3LlzBzdu3PD5GyEhITh8+DAiIyO982pqapCYmDjuOG7fvo2Ghgaf\nr8bGxsk3kIjIwDiSRkQ0Mr/etHb27Fls3Lhx2PyHH34YQUFBsNls3nlhYWEAgO7u7mHLewgh8OGH\nH6K+vh779+8fdxzl5eUoKipSEDkRUeAbOpIGAAUFBcjNzcWOHTsQFHTv+XyjjaSdOnVq3O91+/Zt\ntLW1+cxraWnxT0OIiPzMrwXvqlWrcPXq1RF/lpGRgZ6eHu+0p9CdOnXkbnWXy4WdO3fi6tWrKCsr\nQ0TE+D8dbcOGDUhPT/eZ19LSgtzc3HH/DSIiI+rv7x/xMgWr1Yr6+no899xz3nlDR9JiY2O98z0j\naUMpHUljxwIRBRLNHks2f/581NfXe6cbGhoQHh6OqKioYcu2tbUhLy8Pdrsdx44dw4wZMxS9l8Ph\ngMPh8Jk3ZcqUiQVORGQgRhlJY8cCEQUSzQrezMxM7Nq1C2vXrkVMTAwKCwuRkZEBq9X3MmIhBLZs\n2YJZs2bh4MGDLFSJiIYwykgaOxaIKJBoVvA6nU40NTUhPz8fHR0dSEtLw86dOwEAzc3NePHFF1FV\nVYWWlhacPXsWoaGhSElJ8f7+448/jq+++kqrcImIAo6WI2lERIFE009ay8nJQU5OzrD5sbGx3seO\nxcbGjtp7QUREo+NIGhHRyPjRwkREkuBIGhHRyFjwEhEN0drei5XrPoAjJhEHT9Tjb7nRAfWQdo6k\nEZHR6ZFnNfukNSKiQPBNTRMi4hbDGhSMfzZ34eOj/JRHIiJ/0iPPsuAlIhriWovvI7x+v3ZLp0iI\niOSkR55lwUtENETi3JljThMR0eTokWdZ8BIRDbEteykWzYtAkNWCRfMisC17qd4hERFJRY88y5vW\niIiGiI6Yir2bU/UOg4hIWnrkWfbwEhEREZHUWPASERERkdRY8BIRERGR1FjwEhEREZHUWPASERER\nkdRM85SGgYEBAEBLS4vOkRCR7KKjoxEcbJr06sU8S0RaUpJrTZOR//jjDwDA+vXrdY6EiGRXXV2N\nuLg4vcPQHPMsEWlJSa61CCGEyvEYgsvlwqVLlxAZGYmgoCC9wxlRY2MjcnNzceTIEcTHx+sdjurY\nXnmZqa3A8PaatYc3EPIsYK7900xtBczVXjO1FRi5vezhHYHNZsOyZcv0DmNMfX19AAY3oBl6h9he\neZmprYD52juaQMizgLm2l5naCpirvWZqKzD59vKmNSIiIiKSGgteIiIiIpIaC14iIiIiklrQ7t27\nd+sdBN1js9mQkpKCsLAwvUPRBNsrLzO1FTBfewOdmbaXmdoKmKu9ZmorMLn2muYpDURERERkTryk\ngYiIiIikxoKXiIiIiKTGgpeIiIiIpMaCl4iIiIikxoKXiIiIiKTGgpeIiIiIpMaCl4iIiIikxoKX\niIiIiKTGgtcgSkpKsHjxYixdutT7de7cOb3D8rva2lqkpqZ6p9vb27F582YkJydjzZo1OH78uI7R\n+d/97a2trcXChQt9tnNxcbGOEU7euXPn8NprryE5ORnPPvssjh49CkDebTtae2XctrJhnpXrWPQw\nQ54FzJVrVcmzggzhvffeEyUlJXqHoRq32y2OHz8ukpOTRUpKinf+li1bxPbt24XL5RK//vqrSElJ\nEVeuXNExUv8Yrb3Hjh0Tb7/9to6R+VdbW5tYvny5qKysFAMDA+LSpUti+fLl4qeffpJy247VXtm2\nrYyYZ+U5FoUwT54Vwly5Vq08yx5eg7hy5QoWLlyodxiqKS4uRmlpKTZt2uSd9+eff+L777/H1q1b\nERoaiieeeALp6elSnJ2O1F4A+O2335CYmKhTVP7X3NyMtLQ0ZGZmwmq1YtGiRVixYgXOnz8v5bYd\nq72ybVsZMc/KcywC5smzgLlyrVp5lgWvAXR3d+PatWsoLS3F6tWr8cILL6CiokLvsPzq1VdfRWVl\nJZYsWeKdd/36dQQHByM+Pt47LyEhAXV1dXqE6FcjtRcY/Id7/vx5OJ1OrFmzBvv27UNvb69OUU7e\nwoULsX//fu90e3u7d4hYxm07WnsTExOl27ayYZ6V61gEzJNnAXPlWrXyLAteA2htbUVSUhJef/11\n1NTUYM+ePdi7dy9Onz6td2h+ExUVBYvF4jOvq6sLNpvNZ57NZoPL5dIyNFWM1F4AcDgccDqdOHny\nJMrKyvDLL7+gsLBQhwj9786dO9i0aZP3bFzWbesxtL1Op1PqbSsD5tl7ZDkWzZhnAXPlWn/mWRa8\nBhAfH4/y8nKkpaUhJCQEy5Ytw0svvYTq6mq9Q1NVWFjYsIPS5XLBbrfrFJH6iouLsXHjRtjtdsTH\nxyM/Px+nTp3SO6xJa2xsRHZ2NqZPn46ioiLY7Xapt+397bVardJuW1kwz94j07E4EpmPRTPlWn/n\nWRa8BnD58mUcPnzYZ15PTw9CQkJ0ikgbc+bMQX9/P5qbm73zGhoasGDBAh2jUsGkUBgAAAGgSURB\nVE97ezv27duHzs5O77yenh6EhobqGNXkXb58GevWrUNqaio+++wz2Gw2qbftSO2VddvKhHlWvmNx\nJDIfi2bKtWrkWRa8BmC321FUVITvvvsObrcbP//8M6qqqvDyyy/rHZqqHnroITzzzDP46KOP0N3d\njdraWpw8eRIZGRl6h6aK8PBwnDp1CkVFRejr68P169dRXFyMV155Re/QJqy1tRV5eXnYuHEj3n//\nfVitgylF1m07Wntl3LayYZ6V61gcjazHoplyrWp51s9Pk6AJqq6uFunp6eLJJ58Uzz//vPj222/1\nDkkVZ86c8Xl8zO3bt8XWrVvF8uXLRVpamjh+/LiO0fnf/e2tq6sTb775pkhKShKrVq0Sn3zyiXC7\n3TpGODmHDh0Sjz76qHjqqad8vg4cOCDlth2rvbJtWxkxz8pzLA4le54Vwly5Vq08axFCCBULdSIi\nIiIiXfGSBiIiIiKSGgteIiIiIpIaC14iIiIikhoLXiIiIiKSGgteIiIiIpIaC14iIiIikhoLXiIi\nIiKSGgteIiIiIpLa/wOfsbQLrG3D/QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12d9ecba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tsplot(res_seasonal.resid[12:], lags=24);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Things look much better now.\n",
    "\n",
    "One thing I didn't really talk about is order selection. How to choose $p, d, q, P, D$ and $Q$.\n",
    "R's forecast package does have a handy `auto.arima` function that does this for you.\n",
    "Python / statsmodels don't have that at the minute.\n",
    "The alternative seems to be experience (boo), intuition (boo), and good-old grid-search.\n",
    "You can fit a bunch of models for a bunch of combinations of the parameters and use the [AIC](https://en.wikipedia.org/wiki/Akaike_information_criterion) or [BIC](https://en.wikipedia.org/wiki/Bayesian_information_criterion) to choose the best.\n",
    "[Here](https://www.otexts.org/fpp/8/7) is a useful reference, and [this](http://stackoverflow.com/a/22770973) StackOverflow answer recommends a few options.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Forecasting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we fit that model, let's put it to use.\n",
    "First, we'll make a bunch of one-step ahead forecasts.\n",
    "At each point (month), we take the history up to that point and make a forecast for the next month.\n",
    "So the forecast for January 2014 has available all the data up through December 2013."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred = res_seasonal.get_prediction(start='2001-03-01')\n",
    "pred_ci = pred.conf_int()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HRw/YWJqhoSH/xBrLsqrej2c6E5XOkBYEvGqcaU/9+sLyfTAYZHh4GPCaa2oZ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\ngwIWoYFgT3w7JEqwbUDFNJNg2JujdNaSJNM0gyjIXN4KCOdQWsa+MQ2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/wD/90z/B8zy8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c587710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = y.plot(label='observed')\n",
    "pred.predicted_mean.plot(ax=ax, label='Forecast', alpha=.7)\n",
    "ax.fill_between(pred_ci.index,\n",
    "                pred_ci.iloc[:, 0],\n",
    "                pred_ci.iloc[:, 1], color='k', alpha=.2)\n",
    "ax.set_ylabel(\"Monthly Flights\")\n",
    "plt.legend()\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a few places where the observed series slips outside the 95% confidence interval.\n",
    "The series seems especially unstable before 2005.\n",
    "\n",
    "Alternatively, we can make *dynamic* forecasts as of some month (January 2013 in the example below).\n",
    "That means the forecast from that point forward only use information available as of January 2013.\n",
    "The predictions are generated in a similar way: a bunch of one-step forecasts.\n",
    "Only instead of plugging in the *actual* values beyond January 2013, we plug in the *forecast* values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred_dy = res_seasonal.get_prediction(start='2002-03-01', dynamic='2013-01-01')\n",
    "pred_dy_ci = pred_dy.conf_int()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nOvLIysChU9d1JBKJYXUhOlSsdBKNsMbGxmHPhzmQQCAAn88HQRCQSCQQCoWG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\nQicAAABM9z+CnF+Vg02szAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c8e1ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = y.plot(label='observed')\n",
    "pred_dy.predicted_mean.plot(ax=ax, label='Forecast')\n",
    "ax.fill_between(pred_dy_ci.index,\n",
    "                pred_dy_ci.iloc[:, 0],\n",
    "                pred_dy_ci.iloc[:, 1], color='k', alpha=.25)\n",
    "ax.set_ylabel(\"Monthly Flights\")\n",
    "\n",
    "# Highlight the forecast area\n",
    "ax.fill_betweenx(ax.get_ylim(), pd.Timestamp('2013-01-01'), y.index[-1],\n",
    "                 alpha=.1, zorder=-1)\n",
    "ax.annotate('Dynamic $\\\\longrightarrow$', (pd.Timestamp('2013-02-01'), 550))\n",
    "\n",
    "plt.legend()\n",
    "sns.despine()"
   ]
  },
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   "source": [
    "## Resources\n",
    "\n",
    "This is a collection of links for those interested.\n",
    "\n",
    "### Time series modeling in Python\n",
    "\n",
    "+ [Statsmodels Statespace Notebooks](http://www.statsmodels.org/dev/examples/index.html#statespace)\n",
    "+ [Statsmodels VAR tutorial](http://www.statsmodels.org/dev/vector_ar.html#var)\n",
    "- [ARCH Library by Kevin Sheppard](https://github.com/bashtage/arch)\n",
    "\n",
    "### General Textbooks\n",
    "\n",
    "- [Forecasting: Principles and Practice](https://www.otexts.org/fpp/): A great introduction\n",
    "- [Stock and Watson](http://wps.aw.com/aw_stock_ie_3/178/45691/11696965.cw/): Readable undergraduate resource, has a few chapters on time series\n",
    "- [Greene's Econometric Analysis](http://pages.stern.nyu.edu/~wgreene/Text/econometricanalysis.htm): My favorite PhD level textbook\n",
    "- [Hamilton's Time Series Analysis](http://www.amazon.com/Time-Analysis-James-Douglas-Hamilton/dp/0691042896): A classic\n",
    "- [Lutkehpohl's New Introduction to Multiple Time Series Analysis](http://www.amazon.com/New-Introduction-Multiple-Time-Analysis/dp/3540262393): Extremely dry, but useful if you're implementing this stuff\n",
    "\n",
    "## Conclusion\n",
    "\n",
    "Congratulations if you made it this far, this piece just kept growing (and I still had to cut stuff).\n",
    "The main thing cut was talking about how `SARIMAX` is implemented on top of using statsmodels' statespace framework.\n",
    "The statespace framework, developed mostly by Chad Fulton over the past couple years, is really nice.\n",
    "You can pretty easily [extend it](http://www.statsmodels.org/dev/examples/notebooks/generated/statespace_local_linear_trend.html) with custom models, but still get all the benefits of the framework's estimation and results facilities.\n",
    "I'd recommend reading the [notebooks](http://www.statsmodels.org/dev/examples/index.html#statespace).\n",
    "We also didn't get to talk at all about Skipper Seabold's work on VARs, but maybe some other time.\n",
    "\n",
    "As always, [feedback is welcome](https://twitter.com/tomaugspurger)."
   ]
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